In the initial planning process of flight tests, a method for flight test subject planning based on an improved genetic algorithm is proposed to address the key issue of allocating flight test missions across multiple test aircraft. This method establishes a flight test mission allocation model with the objective of minimizing the total flight test duration. To address the phenomenon of local extrema caused by the number of flight test missions undertaken by the test aircraft becoming fixed after algorithm iterations, a Serial Crossover Mutation (SCM) operator is designed. Comparative results indicate that the improved genetic algorithm employing the SCM operator demonstrates higher efficiency in searching for the global optimal solution compared to other genetic algorithms.