Abstract: The target passive positioning accuracy of UAV swarm is closely related to the aerial topology of UAV swarm, and the target passive positioning accuracy can be effectively improved by optimizing the travel path of UAV swarm. Meanwhile, when the number of UAV swarm sites is large, the path optimization efficiency can be effectively improved and the timeliness of UAV swarm path optimization can be enhanced by using intelligent algorithms. Based on this, this paper proposes a UAV swarm path optimization algorithm based on Cramér-Rao Lower Bound (CRLB), and at the same time adopts Particle Swarm Optimization (PSO) to accelerate the UAV swarm path optimization process, and ultimately realizes UAV swarm path fast and efficiently optimized for target passive positioning. The path optimization is fast and efficient, and the target passive positioning accuracy is improved. First, this paper establishes a target passive positioning signal model and adopts time-difference-of-arrival (TDOA) positioning algorithm for target passive positioning; then, a UAV swarm path optimization algorithm based on CRLB is proposed to optimize the next moment position of UAV swarm site and improve the target passive positioning accuracy by minimizing the CRLB of the target positioning in each moment; again, the UAV swarm path optimization process is accelerated to achieve fast and efficient optimization for target passive positioning and improve the target passive positioning accuracy. target passive positioning accuracy; again, by using PSO intelligent algorithm, accelerate the UAV node position optimization process, improve the path optimization speed and enhance the timeliness of the optimization algorithm. Finally, simulation experiments verify the correctness and effectiveness of the algorithm proposed in this paper.