With a rapid increase in the awareness of carbon reduction worldwide, the industry of electric vehicles (EVs) has started to flourish. However, the large number of EVs connected to a power grid with a large power demand and uncertainty may result in significant challenges for a power system. In this study, the optimal charging and discharging scheduling strategies of G2V/V2G and battery energy storage system (BESS) were proposed for EV charging stations. A distributed computation architecture was employed to streamline the complexity of an optimization problem. By considering EV charging/discharging conversion efficiencies for different load conditions, the proposed method was used to maximize the operational profits of each EV and BESS based on the related electricity tariff and demand response programs. Moreover, the behavior model of drivers and cost of BESS degradation caused by charging and discharging cycles were considered to improve the overall practical applicability. An EV charging station with 100 charging piles was simulated as an example to verify the feasibility of the proposed method. The developed algorithms can be used for EV charging stations, load aggregators, and service companies integrated with distributed energy resources in a smart grid.