Abstract: High-orbit remote sensing satellites have become an indispensable tool in modern remote sensing technology because of their broad field of view coverage， efficient observation timeliness and strong continuous imaging capabilities， which can effectively obtain key feature information of key areas and targets. High-orbit remote sensing satellites often face the application requirements of simultaneous monitoring and tracking of multiple targets in area gaze missions. In order to solve the problem of low task execution efficiency under the demand of multi-objective observation， this paper proposes a high-orbit remote sensing satellite imaging mission planning method based on intelligent optimization algorithm， innovatively designs an "evaluation matrix" as the objective function of the differential evolution algorithm to realize the multi-objective observation area planning， and uses the genetic algorithm to complete the observation path planning on this basis. The simulation results show that compared with the traditional method， the observation efficiency of the proposed method is increased by 28.84% on average， and the energy usage rate is reduced by 24.37% on average. This method can cover all the targets to be tracked with a small number of observations， effectively reduce the number and angles of satellites pointing maneuvers， and the algorithm has good parallelism and portability， which can be adapted to various application scenarios such as on-board autonomous mission planning and constellation cooperative observation.

Key words: high-orbit remote sensing satellite, multi-target detection, observation mission planning, differential evolutionary algorithms, genetic algorithms