To improve the efficiency of the traditional genetic algorithm when the agile satellite observes large-scale ground target points and increase the solution efficiency of intelligent optimization algorithms, the traditional genetic algorithm was improved, and a tabu search-simulated annealing genetic hybrid algorithm was proposed. First, considering the time constraints and attitude orbit dynamics constraints of spacecraft in observing ground target points, the corresponding fitness function was established. The proposed fitness function could guarantee high observation gains and low observation energy consumption, and reflect the observation requirements of practical engineering problems. Subsequently, to improve the mutation process of the traditional genetic algorithm, a tabu search-simulated annealing mutation method was proposed. This mutation method introduced the tabu search method and Metropolis rule in the process of individual mutation optimization. As a result, the tabu search-simulated annealing mutation method could improve the probability of obtaining the optimal global solution, and accelerate the convergence speed of the algorithm. Compared with the traditional genetic algorithm, simulation results showed that the tabu search-simulated annealing genetic hybrid algorithm saved about 40% of the running time. The operating efficiency of the algorithm was also higher than that of other improved genetic algorithms such as simulated annealing genetic algorithm and tabu search genetic algorithm. The results verified the efficiency of the tabu search-simulated annealing genetic hybrid algorithm in solving the mission planning problem of agile observation satellite.