In recent years, the problem of multi-UAV multi-target mission planning has attracted much attention, which includes two core stages: task assignment and Co-allocation, as well as track planning. The conventional method generally adopts the selection of appropriate task assignment strategy and the combination of Traveling salesman problem algorithm for track planning. In this study, Pelican intelligent optimization algorithm is applied to the task planning of multiple UAVs. By comparing the total path, this method finds the combination of task assignment and flight path planning with the shortest path. Based on the background of reconnaissance mission, a Co-allocation strategy of multi-UAVs cooperative reconnaissance mission planning decision is constructed by introducing common assignment strategy, and MATLAB is used for modeling calculation and simulation. To verify the effectiveness of this algorithm, Pelican intelligent optimization algorithm calculates and simulates three sets of task planning results. At the same time, in order to verify the accuracy of the proposed method, the multi-machine multi-objective task planning results of the conventional method are calculated and simulated for the same set of data. In the conventional method, integer programming algorithm is used for task assignment and ant colony algorithm is used for flight path planning. By comparison, it is found that the total distance of the three groups of results based on Pelican intelligent optimization algorithm is 32488.74, 32469.97 and 32257.32 respectively. The total distance of a set of planning results obtained by the conventional method is 32557.70. The experimental results show that Pelican intelligent optimization algorithm can obtain shorter task planning routes, thus verifying the effectiveness and rationality of the algorithm.