A genetic algorithm array structure optimization scheme for Hanbury and Brown-Twiss (HBT) interference localization is proposed for the problem that the acoustic localization performance is easily affected by the microphone array structure. The method constructs intermediate populations with the distance difference between two adjacent array elements as individuals, and converts them to distance-spaced populations by gene sorted by ascending order. Then, the objective function based on the maximum parametric level is constructed with the directional map function as the fitness function, and the microphone spacing is taken as the optimization object to transform the objective function into an unconstrained optimization problem. The problem is solved by genetic algorithm to obtain the array structure with the highest localization performance. The simulation results show that the optimization effect of the seven-element array is the most significant, and the peak value of the near-point positioning partials is reduced from 0.4374 to 0, and the number of distant-point positioning interference partials is reduced to 0. This method can effectively improve the positioning accuracy.