The shift towards sustainable transportation systems underscores the need to enhance the routing of electric vehicles (EVs). This work delves into the Electrical Vehicle Routing Problem (EVRP), by considering the unique requirements of electric vehicles, such as battery range limitations and the availability of charging stations. A significant addition to this work is the inclusion of different driving modes such as Eco, Ride, Sport, and Warp which influence energy consumption rates and travel times. Also, it aims to optimize routes not only for efficiency but also by factoring in these variable driving modes to adapt to drivers’ preferences or situational needs. The A* algorithm is employed for its effectiveness in quickly determining the shortest and least costly path by estimating future costs. This algorithm’s performance is compared with that of the Ant Colony Optimization (ACO) algorithm, which mimics the way ants find efficient paths. Our comparative analysis seeks to justify the obtained result along with swiftness handling the complex of EVRP, including varied vehicle modes and accurate time estimations. Further, the customized version of the routing problem for both EV bikes and EV cars with the consideration of speed modes, State of Charge (SOC), discharge level calculation, energy used, and time of travel is optimized. Also, EV route-based feasibility and charging metric analysis are performed.