Electric Vehicles (EVs) play a pivotal role in advancing environmental sustainability and accelerating the transition toward clean energy systems. However, large-scale EV adoption poses significant operational challenges, particularly when charging and discharging activities are uncoordinated, potentially leading to elevated peak demand and increased grid stress. Effective scheduling techniques are therefore essential to ensure reliable integration of EVs into modern power systems. This study provides a rigorous comparative evaluation of two metaheuristic optimization paradigms for EV charging and discharging scheduling: the traditional Particle Swarm Optimization (PSO) algorithm and the more recent Transit Search Optimization (TSO) algorithm. Using an identical system configuration and EV dataset, the study assesses the performance of both approaches based on peak power reduction, cost minimization, and overall system efficiency. Results demonstrate that while enhanced PSO scenarios exhibit noticeable improvements over earlier literature, TSO consistently achieves superior outcomes due to its stronger exploration-exploitation balance. In particular, TSO attains a 46.23 % reduction in average EV charging cost and achieves the lowest power-loss levels across all tested scenarios. Relative to the best previously published benchmarks, TSO further improves peak power consumption by 1.6 % and total charging cost by 6.1 %. These findings highlight TSO’s strong potential as a high-efficiency scheduling tool for large-scale EV integration in future smart grid environments.