Flying probe testing machines have a long detection time and low test efficiency, and their probes are easy to strike in single probe detection when detecting circuit boards. Therefore, a test path planning algorithm based on an improved particle swarm optimization algorithm is proposed. Firstly, the collision between two probes is solved by partition detection. Secondly, an improved particle swarm optimization algorithm is proposed, and a chaotic initialization formula is added to constrain and update the maximum speed of search based on the particle swarm optimization algorithm. In addition, the idea of crossover and variation of the genetic algorithm is introduced to improve some defects that the particle swarm optimization algorithm tends to fall into local optimum, which enhances the global search ability of the algorithm. The effectiveness of the proposed algorithm, particle swarm optimization algorithm, and genetic algorithm is compared and analyzed, and real machine tests are carried out. The results show that the proposed algorithm can effectively solve the collision between two probes during the tests. Compared with the other two algorithms, the improved particle swarm optimization algorithm has a stronger global search ability while reducing the number of iterations, and it can reduce the algorithm operation time by 30% and the test distance by 10%, which has a certain engineering application value.