Efficient operation of Battery Swapping Stations (BSS) is critical to supporting the widespread adoption of Electric Vehicles (EVs). This paper investigates the performance of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) in optimizing BSS operations under a Time-of-Day (ToD) tariff scheme. Three configurations–small, medium, and large BSS–are analyzed, focusing on minimizing costs and ensuring a balanced battery distribution. Simulation results demonstrate that PSO consistently achieves lower operational costs compared to GA, with cost savings of up to 7% in large BSS configurations. Additionally, the state of charge (SoC) distribution analysis highlights the adaptability of the optimization framework to varying demand scenarios, ensuring adequate battery availability across all charging levels. These findings position PSO as a robust and cost-efficient tool for BSS management, paving the way for scalable and sustainable EV infrastructure solutions. Future work aims to extend this framework to include renewable energy integration and battery degradation modeling for next-generation BSS systems.