Considering the balance among economic, environmental, and social benefits in ride-hailing operations, this study proposes a multi-objective schedule model that balances these three benefits, as well as an algorithm based on dynamic space programming. The model integrates traditional taxi services and shared transport for the first time, comprehensively covering four different interaction scenarios between drivers and passengers, to achieve synergistic improvement of the three benefits through optimization strategies. The algorithm creatively combines the lapjv algorithm and the branch and bound method to ensure that the optimal matching strategy satisfying multi-objective optimization can be efficiently explored and determined under the given threshold constraints. Compared with SCIP, the average error of the algorithm is within 4%, and the average solving speed is improved by 99.1%. This study systematically applies this algorithm to solve and generate Pareto frontier graphs for different threshold constraints, intuitively displaying the trade-offs and changing trends of one of the three objectives (economic, environmental, and social benefits) under the constraints of the other two objectives. This study provides a decision-making basis for ride-hailing operations.