Electric vehicles (EVs) are gaining popularity due to their environmental advantages over traditional vehicles. While considerable research has addressed EV performance, safety, and efficient grid power utilization, there is a noticeable lack of studies focusing on charging station capacity and Time of Use (TOU) power grids in relation to transport routes. This chapter seeks to fill this gap by introducing a novel approach for optimizing EV charging and scheduling using Grey Wolf Optimization (GWO). The proposed method accounts for various factors including route length, vehicle capacity, traffic congestion, charging time, travel time, backup requirements, and cost considerations. A comprehensive simulation platform has been created to facilitate the management of large-scale EV deployments under real-world charging conditions. This platform is instrumental in designing optimal strategies to alleviate congestion at charging stations and improve overall EV usage efficiency. Simulation results demonstrate significant enhancements in the utilization of EVs, optimization of TOU grid power, and reduction of charging station congestion. Our approach ensures the economical use of grid power and offers a structured strategy for scheduling EVs based on real-time data and route-specific parameters. By effectively balancing charging demand with grid supply, the method reduces waiting times at charging stations and mitigates traffic congestion caused by multiple EVs arriving simultaneously. The integration of backup plans and cost considerations provides a thorough strategy for efficient EV deployment, leading to lower operational costs and improved environmental sustainability.