Objectives  This study is focused on improving the performance and increasing the detection probability of a multiple Unmanned Underwater Vehicle （UUV） seabed acoustic beacon searching a specific sea area.

  Methods  First, the search ability index function of the passive sonar is given. Second, the Monte Carlo method is used to randomly simulate the coordinate position of the submarine acoustic beacon. Third, the cluster search capability function is established and the optimization goal of the optimization task is obtained. The optimization constraints are established in combination with the formation requirements of the UUV actually performing the task. Finally, the integration is based on the seabed acoustic beacon search probability. The cluster formation optimization model is maximized and the UUV cluster is made to complete the acoustic beacon search work in the specified area according to this formation. In this paper, the genetic algorithm is used to optimize the parameters of the optimization model. By setting a reasonable objective function and improving the traditional genetic operator, the value of the objective function reaches the set standard. At this point, the corresponding parameters are taken out to complete the value selection.

  Results  By comparing this new optimized formation with the traditional optimized formation, it is found that the optimized formation has a higher average probability of finding the submarine beacon.

  Conclusions  The optimized model test results show that the proposed method can effectively improve the submarine acoustic beacon search performance of UUV clusters and provide a reasonable formation optimization scheme.