Thanks to the advantages of zero carbon dioxide emissions and low operation cost, the number of on-road electric vehicles (EVs) is expected to keep increasing. They usually get charged through charging stations powered by either the grid or renewable plants. Due to the potential difference in electricity price between the grid and the renewable plants, an EV may purchase electricity at charging stations powered by renewable plants, and then discharge the surplus energy in the battery to the grid, to gain profits and enhance the overall renewable energy utilization. In this work, we aim to optimize the route selection and charging/discharging scheduling to improve the overall economic profits of EVs, taking into account the constraints, including the time-varying energy supply caused by the intermittent generation of renewable energy, the limited number of charging piles in a charging station, and the traveling delay tolerance of EVs. Firstly, a time-expanded vehicle-to-grid graph is designed to model the objective and related constraints. Then, we apply an AI-based A* algorithm to find K-shortest paths for each EV. Finally, a joint routing selection and charging/discharging algorithm, namely, K-Shortest-Paths-Joint-Routing-Scheduling (KSP-JRS), is proposed to minimize the total cost of EVs by maximizing their revenue from energy discharging under time constraints. The proposed approach is evaluated using the real traffic map around Santa Clara, California. The simulation, with different numbers of testing EVs, shows the feasibility and superiority of the proposed algorithm.