Bombers are an important platform for air to surface combat against enemy targets, and their aircraft firepower distribution is crucial in combat. When traditional genetic algorithms are used to solve aircraft firepower distribution problems, many individuals who do not meet the constraint conditions will be generated during the evolution process, which affects the search and optimization efficiency of the algorithm. This paper presents an enhanced chaotic adaptive genetic algorithm to tackle this problem. By utilizing the characteristics of randomness and traversal of chaotic optimization algorithms, the problem of premature convergence in genetic algorithms that are prone to getting stuck in local optima has been solved. By combining the constraints of the aircraft firepower distribution problem with the coding mode of chromosomes, the selection, crossover and mutation operators are improved accordingly, enabling the novel method to possess robust neighborhood and overall exploration capabilities. The examples demonstrate that the technique is straightforward and rapid, with significantly better solving quality and efficiency than the standard genetic algorithm.