Traditional line-bound bus operations are challenged by the mismatch between passenger demand and transport capacity. They also limit the flexibility of public transport services. To address these issues, cross-line scheduling and interlining scheduling were introduced. Cross-line scheduling was used to integrate existing bus resources to improve operational efficiency. Interlining scheduling was applied to provide flexible travel services and enhance the overall service capability of the system. A regional scheduling optimization model for electric buses was established based on the combined cross-line and interlining scheduling approach. The model aims to minimize bus operation costs and maximize the smoothness of departure intervals. Timetable constraints and trip chaining relationships were considered. A hybrid algorithm of MOPSO-GA was developed by combining multi-objective particle swarm optimization(MOPSO) and genetic algorithm (GA) to solve the model. Three bus routes in Beijing were selected as case studies to verify the feasibility and effectiveness of the proposed model and algorithm. The results show that the bus operation cost is reduced by 11.18%. The smoothness of departure intervals is improved by 10.07%. The original line departure interval smoothness increases by 12.33%. The required fleet size is reduced by 17.85%.