The rapid growth in electric vehicle (EV) adoption underscores the urgent need for strategically located charging infrastructure to ensure reliable access, reduce range anxiety, and promote sustainable urban mobility. This study explores the application of the Whale Optimization Algorithm (WOA), a bio-inspired meta-heuristic technique, for optimizing the siting of EV charging stations within a metropolitan road network. The problem is formulated as a single-objective optimization model aimed at minimizing the total travel distance between EV users and the nearest charging station, while implicitly considering factors such as spatial coverage and equitable distribution. A case study of Guwahati, India, is undertaken to validate the proposed approach. The urban road network is modeled using graph theory, and real-world data on population density and projected EV growth are incorporated into the simulation. MATLAB is used to implement the WOA and evaluate its performance across different EV demand distribution scenarios. Results demonstrate that WOA significantly improves siting efficiency, reducing both cumulative user travel distance and daily energy demand when compared to baseline configurations. The study provides a computationally efficient and scalable framework for urban planners and policymakers seeking to deploy EV charging infrastructure in a manner that balances user convenience, operational efficiency, and future urban growth trajectories.