## 1 Introduction
The results show that the optimization effect of the GA algorithm is better than the particle swarm optimization (PSO) algorithm in a limited planning time. [PERSON] studied the backtracking heuristic algorithm based on the constrained programming model [12]. [PERSON] et al. designed a new tabu search algorithm to solve the problem when considering task priority, resource constraints, and user satisfaction in the study of mission planning for low-orbit satellites [13]. [PERSON] conducted mathematical modelling based on various constraints of the task planning process, and then proposed a rolling planning heuristic algorithm for the model, which divided the global task planning process into successive local planning tasks [14]. [PERSON] et al. designed a multi-objective genetic algorithm for fast response to natural disasters [15]. [PERSON] and [PERSON] established the mission planning model by considering the synthetic observation between missions; to maximize the completion of the task as the goal of optimization, a fast model annealing algorithm and a dynamic task synthesis heuristic algorithm were proposed to solve the problem [16]. Considering the priority of the task, [PERSON] and [PERSON] [17] designed a planning model and solution algorithm. [PERSON] et al. proposed a parallel ant colony algorithm and proved its high efficiency through experiments [18]. Figure 1: Task clustering diagram [1]. [PERSON] solved the problem of massive user requests by designing a task merging mechanism using an adaptive ant colony algorithm [19]. Satellite task planning is a complex combination of multi-objective optimization problems. The optimization results of traditional algorithms are usually not ideal. The intelligent search algorithm is highly versatile, does not depend on specific problems, has a high search efficiency, and has an inherent parallel search capability. It is a desirable and suitable algorithm for large-scale applications. However, if a single intelligent search algorithm is used, a premature phenomenon falling into a local optimum is generally found, and the improvement of a single algorithm has some limitations. Aiming to avoid the premature phenomenon and extend the application range, a hybrid strategy combining multiple intelligent search algorithms was designed. Previous studies mostly adopted a single algorithm to solve the problem, but the optimization performance and algorithm stability were not good, and there were few constraints considered in optimization, which is quite different from the actual situation. Thus, due to the above two cases, this paper adopts a genetic algorithm-annealing algorithm (GA-SA) hybrid optimization algorithm, aiming to maximize the priority and the number of completions and considering the mission constraints including the satellite visible window, the sensor observation without overlapping, the preparation time for the sensor operation, slew angle, the observation time of the task, the single longest boot duration, the single maximum boot time, satellite energy, and data storage. A hybrid algorithm of GA and SA was developed to improve performance of satellite observation, which inherits fully the advantages of each single algorithm and the algorithms complement each other. Finally, the experimental simulation results show that the improved clustering algorithm combined with the GA-SA hybrid planning algorithm have a higher observation performance, thus improving the mission execution efficiency.