The stopping scheme for passenger trains is important to the operation planning of trains, and the scheme affects the quality of passenger service and the transportation efficiency. This study established a multi-target programming model, aiming to minimize the total travel time of passengers and maximize the zone accessibility. In view of the traditional Particle Swarm Optimization (PSO) algorithm, which is inefficient and easy to fall into local optimum and cannot effectively handle the discrete problems when dealing with complex high dimensional problems, a new hybrid particle swarm algorithm is proposed based on the Quantum Genetic Algorithm (QGA). First, the algorithm adopted the construction of particle swarm algorithm, employing the idea of quantum bit coding, and using PSO algorithm velocity update mechanism to update the quantum revolving door. Since the algorithm combined the global exploration of QGA and intelligent system PSO populations, which not only improves the convergence speed of algorithm, but also increases the diversity of particle. Finally, the experiment on the ZDT function optimum and stopping scheme optimum problem shows that the proposed algorithm consistently provides faster convergence and precision.