Energy consumption has always been a hot topic in the research of rail transit. When the train adopts regenerative braking for braking, the regenerative braking energy(RBE) generated is fed back into the traction power supply network, causing a sudden increase of voltage. If this part of the energy can be utilized, it will greatly reduce the operating cost of the train. Permanent magnet maglev trains have the advantages of zero levitation energy consumption, strong climbing ability, low noise, and having broad prospects for future development. This paper proposes a multi-train collaborative energy-saving speed curve optimization algorithm based on the utilization of RBE. Firstly, a train dynamics model is established, then optimization model of the train speed curve is established with the goals of punctual train operation and energy conservation, and the speed limit and passenger comfort are considered as constraints. Based on the start and end times of the braking train, the speed curve of the collaborative train is divided into three stages in terms of time. A genetic algorithm is introduced to search and optimize the key state parameters of speed curve. Finally, the algorithm was verified based on the actual data of Jiangxi Xingguo’s permanent magnet maglev line and compared with the single-train energy-saving speed curve algorithm. The results show that the designed collaborative energy-saving algorithm can quickly generate the optimal energy-saving speed curve, with an energy-saving rate of approximately 26%.