Concerning the problem of traditional Particle Swarm Optimization (PSO) such as slow convergence and being easy falling into local extremum, a Dynamic self-Adaptive Chaotic Quantum-behaved PSO (DACQPSO) was proposed by studying the relationship between population diversity and the evolution of PSO. The population-distribution-entropy was introduced into the evolutionary control of the particle swarm in this algorithm. Based on the Sigmoid function model, the method of calculating the contraction-expansion coefficient of the Quantum-behaved PSO (QPSO) was given. The average-distance-amongst-points was taken as the criterion of chaotic search to carry out a chaotic perturbation. The DACQPSO algorithm was applied to the coverage optimization of Wireless Sensor Network (WSN), and the simulation analysis was carried out. Experimental results show that compared with Standard PSO (SPSO), QPSO and Chaotic Quantum-behaved PSO (CQPSO), the DACQPSO algorithm improves the coverage rate by 3.3501%, 2.6502% and 1.9000% respectively. DACQPSO algorithm improves the coverage performance of WSN, and has better coverage optimization effect than other algorithms.