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In the application of the rotor unmanned aerial vehicle (UAV) cluster, the quick selection of a predetermined point of each UAV plays a great role for improving the overall performance. In the case of reaching the target airspace point and then landing, it can be regarded as the best matching problem of the bipartite graph, which can be solved by the Hungarian algorithm. In the case of reaching the airspace point then waiting for the other UAVs to work together after they are fully in place, it is necessary to optimize both of the coupling parameters: the mean moving distance and the maximum moving distance at the same time. The value of elemental in the cost matrix of the Hungarian algorithm is replaced after experiments and tests, so that the maximum moving distance can be reduced when the average moving distance is very close to the optimal solution, and the time to complete the process can be shortened. Genetic algorithm for this problem was designed to compared with the improved algorithm. It is verified that the improved Hungarian algorithm performs better in this problem. Due to the fast calculation of this algorithm, it is suitable for real-time calculation of embedded system in UAV.