In recent years, Electric Vehicles (EV) received many adaptation and improvements which make them suitable for real life. The popularity of them demands more electricity and consequently smart charge scheduling algorithms to reduce driving cost and peak loads in charge stations. To address this challenge, we propose a smart charging scheduler based on mathematical optimization to determine the optimal charging volume from charging stations in a given route. The addressed problem formulated as a Mixed Integer Linear Program (MILP) to choose the optimal charging points between an origin and destination. This problem takes predicted time slot price signals and vehicles information to calculate the optimal solution. To deal with NP-hardness of MILP formulation, the proposed model solved using a heuristic simulated annealing (SA) algorithm. In the end, we evaluate the benefit of such scheduling using the Southern California Edison average hourly price. The results show that this schedule can contribute to EV owners and charge stations interests.

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