In order to improve the network performance, to increase network coverage rate, to achieve the maximization of network coverage and extend the network of life for the gas outburst detection system in underground coal mine goaf, the present research has proposed a Wireless Sensor Networks (WSNs) efficient coverage optimization strategy on the basis of probability measuring model. Although various optimization techniques have been applied to obtain values for the network coverage, we choose a stochastic particle swarm optimization (SPSO) algorithm to perform an optimal design. Computational performances of different methods are investigated and the influence of swarm size on the optimization problem of coverage is examined. The influences of the sensor errors on computational accuracy are compared. Through the SPSO, the strategy achieves coverage control for the optimization objectives of network coverage rate and analyzes the effect of coverage performance. The results show that the effective coverage rate of WSN has increased, the proposed algorithm can guarantee the convergence of the global optimization solution, and is proved to be superior in terms of searching quality and speed.