With the development of human economy and the increase in energy demand, the strategic position of the ocean has become increasingly prominent, requiring efficient monitoring of China's waters. This can not only improve the control of China's waters, but also cooperate with ocean operations, target tracking, and seabed exploration, among which the optimized deployment technology of the monitoring system is the key. For the test engineering tasks of underwater acoustic monitoring deployment, offshore construction operations are difficult, and the underwater acoustic monitoring node deployment plan has low monitoring coverage area utilization and positioning accuracy. To solve the error problem, this paper designs a multi-swarm intelligent deployment algorithm based on heuristics and grid processing to maximize effective detection coverage of the underwater mission area. A comprehensive evaluation of effective coverage under multiple constraints was established to analyze and evaluate the optimal deployment effect. The simulation results show that MQPSO (Multi-phase Quantum-behaved Particle Swarm Optimization) algorithm has a relatively stable global search capability. Compared with manual deployment and PSO (Particle Swarm Optimization) algorithm, the detection effect of MQPSO algorithm is obvious. Improvement provides strong support for underwater detection tasks.