Abstract: The target passive positioning accuracy of unmanned aerial vehicle swarm is closely related to the aerial topology structure of the unmanned aerial vehicle swarm. By optimizing the travel path of unmanned aerial vehicle swarm, the target passive positioning accuracy can be effectively improved. Meanwhile, when there are a large number of unmanned aerial vehicle swarm sites, the efficiency of path optimization can be effectively improved and the timeliness of unmanned aerial vehicle swarm path optimization can be enhanced by intelligent algorithms. Based on that, a path optimization algorithm for unmanned aerial vehicle swarm is proposed in this paper, which is based on the Cramér-Rao low bound (CRLB). The particle swarm optimization (PSO) algorithm is also employed to accelerate the path optimization process of the unmanned aerial vehicle swarm, ultimately achieving fast and efficient optimization of unmanned aerial vehicle swarm path for target passive positioning, and improving the accuracy of target passive positioning. First, a passive target positioning signal model is established in this paper, and the time-difference-of-arrival positioning algorithm is used for target passive positioning. Subsequently, a path optimization algorithm for unmanned aerial vehicle swarm based on CRLB is proposed. By minimizing the CRLB of target positioning at each moment, the next moment′s position of the unmanned aerial vehicle swarm site is optimized to improve the accuracy of target passive positioning. Then, through the PSO intelligent algorithm, the process of optimizing the position of unmanned aerial vehicle nodes is accelerated, while the speed of path optimization is improved and the timeliness of optimization algorithm is enhanced. Finally, the correctness and effectiveness of the proposed algorithm are verified through simulations.