In the face of gradient operation conditions, the development of control strategies that simultaneously take into account intervehicle cooperative control and energy economy is one of the key technologies for improving traffic efficiency and exploiting the energysaving potential of vehicles. A hierarchical optimization control strategy based on improved particle swarm optimization algorithm and Q-learning for fuel cell hybrid electric vehicles queue is proposed with the objective of safe driving and optimizing energy consumption. In this strategy, the upper layer controller uses the improved particle swarm optimization algorithm to obtain the energy-saving speed trajectory under the premise of ensuring that safety constraints such as distance or speed limit from the preceding vehicle are satisfied, and utilizes the model predictive control framework to adjust the vehicle speed in real time to ensure the vehicle follows the energy-saving speed trajectory. The lower layer controller builds the Q-learning controller based on the information such as vehicle speed and demand power solved by the upper layer to realize the optimal energy distribution between the fuel cell hybrid electric vehicles power cell and the fuel cell. Simulation results show that the hierarchical control strategy proposed in this paper exhibits good tracking performance and safety performance under slope conditions, and the optimization results are similar to the dynamic planning strategy, indicating the energy consumption economy and feasibility of the strategy.