Optimal Placement and Cost Analysis of Electric Vehicle Charging Stations Using Metaheuristic Optimization

The rapid adoption of electric vehicles (EVs) has made the strategic deployment of charging infrastructure a critical task for sustainable mobility. This study formulates the siting of EV charging stations as a p-median problem and applies two metaheuristic approaches—genetic algorithm (GA) and ant colony optimization (ACO)—to solve it. The cost function, defined as the combination of transportation and installation costs, was analyzed in various scenarios. The results show that ACO consistently outperforms GA, offering lower total costs and shorter solution times. Crucially, the work uses optimization results published in the literature to expand the comparison beyond GA, using GA as a typical baseline. The suggested framework is adaptable and can be used to solve different spatial planning and facility location issues. This paper offers a data-driven, scientifically based approach for EV charging infrastructure development by combining cost effectiveness and service accessibility. In addition to providing decision-makers with useful tactics for creating dependable and sustainable charging networks, it helps handle the temporal and geographical coordination issues in EV charging.