Abstract: This study aims to solve the task assignment problem of the attack source node location in the industrial wireless sensor network by establishing and resolving an attack source node location task assignment optimization model. In the task assignment optimization model, the total energy consumption and distance standard deviation of the reference node combination are set as the objective functions and the space and residual energy constraints are set as the conditions. The hybrid optimization algorithm, which is the combination of the NSGA-Ⅱ algorithm improved by cyclic crowd sorting and the sparsity local search, is adopted to solve the task assignment optimization model. The solution with the least sparsity is considered the sparse solution. Furthermore, the limit optimization strategy is adopted to process the sparse solution to obtain the final solution with better distribution characteristics. The simulation conducted with Matlab shows that the improved hybrid optimization algorithm can improve the convergence speed of the algorithm and reduce the complexity of the algorithm. Moreover, the improved hybrid optimization algorithm can rapidly select the appropriate reference node combination, which reduces the positioning error and improves the positioning accuracy.