Satellites and ground observation resources can use their respective loads to complete tasks such as disaster forecasting, environmental monitoring and target discovery. Ground observation resources can cooperate with satellites to complete observation tasks and improve task observation effects. The planning algorithm allocates numerous observation tasks to limited satellites and ground observation resources for execution, which can improve the observation effect. It is a challenge to obtain a mission execution plan for satellite and ground observation resources at the same time. This paper studies the satellite-ground coordinated observation planning problem(SGCOPP), constructs a mathematical model of mission planning, and proposes an improved artificial bee colony(IABC) algorithm and a satellite-ground resource coordinated time selection algorithm(SGRCTSA) based on the characteristics of the problem. This algorithm improves the process of initial population generation, colony optimization process and individual elimination process to enhance its search optimization ability. A large number of experiments verify the effectiveness of the proposed IABC algorithm to solve the SGCOPP. Results show that the IABC algorithm performs better than state-of-the-art algorithms, which can provide technical support for cross-domain collaborative observation research.