In order to improve the efficiency of multi-unmanned aerial vehicle (UAV) task allocation and get a better allocation scheme, this paper proposes a multi-UAV task allocation method based on the improved grey wolf optimization algorithm. The improved grey wolf optimization algorithm improves the grey wolf optimization algorithm for the two problems of slow convergence speed and easy to fall into the local optimum when dealing with part of the problem, and effectively improves the two problems through the self-adaptive adjustment strategy, the control parameter nonlinearization strategy, and the stochastic dispersion strategy, and demonstrates the optimal task allocation fitness function value along with the simulation of task allocation of six unmanned aerial vehicles with eight targets. The value of the optimal task assignment fitness function gradually decreases with the increase of iteration number and tends to be stable within a certain number of iterations, which verifies the effectiveness of the algorithm and meets the initial expectation.