<p>In recent years, unmanned aerial vehicles (UAVs) reconnaissance task allocation are attracting more and more research attention. The efficient allocation of UAV resources is a fundamental and challenging problem. In this paper, the heterogeneous reconnaissance targets are categorized into point, line, and area targets. Considering the constraints of UAV flight path and remaining resources, we construct a multi-objective optimization model with fuel cost and total task time cost as the optimization objectives. To solve this model, an improved multi-objective gray wolf optimization (IMOGWO) algorithm is proposed, which employs three novel improved strategies to balance the exploration and exploitation abilities. Firstly, a nonlinear convergence factor is designed to strengthen the global search ability of the algorithm. Secondly, an evolutionary strategy is introduced to improve the population diversity to help the population jumps out of the local optimum. Finally, a Pareto front optimization strategy is adopted to remove the sub equivalent solutions and maintain the Pareto front set. Compared with the popular and classic multi-objective algorithms, the simulation results verify the effectiveness and superiority of the IMOGWO algorithm in solving the task allocation problem. Furthermore, its superiority becomes more pronounced as the problem scale increases.</p>