The present invention provides a multi-satellite mission planning method based on K-means clustering, S1, collecting the user's mission requirements T={t 1 ,t 2 ,t 3 ...t n}, to obtain the set of orbital working hours of each sunlit area corresponding to all currently available satellites O={o 1 ,o 2 ,o 3 ,...o m}. S2, computing task t i to each element o in the set O j The distance Dis ij , forming a task t i The distance set D={d to the orbital set O i1 , d i2 , d i3 ... d in}, the task t i Clustering to the shortest orbital k,Dis ik =Min(D); S3, judging the current clustering scheme s k Whether it belongs to the set S={s 1 ,s 2 ,s 3 ,...s z}, if s k ∈S then output the clustering scheme s k , otherwise the scheme s k Add to the scheme set S, and return to step S2. The present invention quantifies these factors by analyzing the factors affecting multi-satellite task allocation, and combines the K-means clustering algorithm to plan a multi-satellite collaborative task allocation scheme, with fewer iterations and fast calculation speed, which can meet large-scale optimization problems Constrains the time complexity of the algorithm, and greatly improves the quality of imaging and the completion rate of tasks.