Finding an optimal path between a source and target node in a graph or network is a well-established problem in Computer Science. This problem has been studied extensively, with a range of algorithms developed to accommodate diverse objectives, domains, and real-world application. Classical approaches, such as Dijkstra’s algorithm, are well-known for computing a single optimal path based on one objective, variable or criteria. This is typically distance or time and algorithms like this are not designed to handle multiple objectives or generate a usable set of diverse alternative routes. In the case of transportation networks, as urban environments become more complex and mobility expectations increase, the ability to consider multiple objectives and offer diverse routing options on road networks has shifted from a theoretical challenge to a practical necessity. A real-world example of this might be a driver who wishes to travel between two points in a road network and is seeking a route which minimises travel distance, maximises the number of EV charging stations available on the route, and minimises the overall cost of the journey in terms of toll charges. Traditional algorithms (such as Dijkstra’s) cannot solve scenarios where a path (or route) has multiple objectives to optimise. While such methods are effective at returning a single shortest path, many real-world applications require a set of optimal (or nearoptimal) alternative paths. Algorithms such as the k-shortest paths algorithm improve upon, and extend, earlier approaches by producing multiple paths of equal or near-equal length between two points. Nevertheless, most algorithmic approaches remain limited to single-objective optimisation and do not guarantee diversity among the final set of generated paths. To address these limitations, population-based methods such as Evolutionary Algorithms (EAs) have emerged as promising alternatives, capable of producing diverse and/or multi-objective solutions suited for the challenges outlined above. In this thesis, we make several contributions to this rapidly growing research field by introducing novel computational frameworks capable of generating diverse, multiobjective routes or paths in large-scale real-world transportation networks. This thesis introduces a novel framework for solving the K-Most Diverse Near-Shortest Paths (KMDNSP) problem, which aims to find a set of k near-optimal paths that maximise path diversity while having near-optimal overall route lengths. To achieve this we have used a special class of EAs known as Parallel Genetic Algorithms (PGAs). The proposed PGA, named the MultiPath Island-Based Genetic Algorithm (MIBGA), serves to facilitate vehicle routing mechanisms for the KMDNSP problem. This effectively balances the need for path diversity and optimality through innovative migration and adaptive selection strategies. Empirical results presented confirm the efficacy of MIBGA and demonstrate how its design supports the generation of diverse, near-shortest paths. Building on the island structure of MIBGA and integrating it with a global parallelisation model, we present the Parallel Optimal-route Search (POS) as a multi-objective routing framework. POS is designed to address the real-world routing problems in the context of general public mobility needs by providing end users with a collection of optimal paths optimised across multiple criteria. To achieve this, a novel fitness metric called Positional Count (PC) is introduced to mitigate the bias caused by longer routes potentially offering better values for certain objectives. PC assigns weights to features based on their position and density along the route, shifting the focus from sheer quantity to the contextual relevance and spatial distribution of features along the route. To demonstrate the robustness of our methods across different network topologies and scales, we perform evaluation on complex real-world road networks rather than the simpler synthetic networks commonly used in much of the key literature in this research. The smallest network tested is from Arizona, with 834 nodes and 1, 547 edges, while the largest is Texas, featuring 10, 886 unique nodes and 24, 464 edges. Our framework for solving the KMDNSP problem, the development of MIBGA and POS demonstrate significant contributions to the area of path optimisation on real-world transportation networks. A number of opportunities for future work are presented at the end of the thesis and these address theoretical, practical, and application-oriented aspects of the proposed methods and metrics in this work. All software code and network data used in our research is available as open-source resources to enhance the opportunities for reproducibility and reuse.