In the long-baseline positioning of submerged buoys, the geometric structure of deployment significantly impacts positioning accuracy. Meanwhile, overly dense deployment of measurement stations has limited improvement on positioning accuracy, leading to redundant measurement station resources. To enhance positioning accuracy performance under limited measurement station resources while ensuring effective coverage of core areas, this paper proposes the use of an improved Particle Swarm Optimization (PSO) algorithm for iterative optimization of deployment positions. First, to avoid the optimization results from falling into local optimal solutions, the inertia weight was improved. Second, to ensure the coverage capability of the measurement area, the proportion of unmeasured areas was used as a penalty function to guide the array to cover as large a measurement area as possible. The simulation calculation results show that, the measurement accuracy of the underwater beacon array for pulse sound signals has been improved after optimization, with the coverage rate of three beacons increasing from 74.7% to 89.2%, and the coverage rate of four beacons increasing from 41.1% to 70.2%, while maintaining stable measurement accuracy. Meanwhile, this method has the potential for further application in different scenarios of long baseline positioning for submerged buoys.