In view of the threat of enemy air attack and the efficiency of solving the task assignment problem of medium-scale air defense firepower, a chainlike multi-population genetic algorithm (CMPGA) with superior performance was proposed. First, an improved air defense firepower task allocation model was established, which comprehensively investigates the threat degree of target and the interceptability judgment. The target threat degree was studied in terms of the threat factors such as the height, speed, range, and relative distance of the target. The time constraints, space constraints, and performance constraints were considered in the interceptability judgment, which was integrated into the kill probability to simplify the constraints of the model. Then, the CMPGA algorithm was proposed to solve the optimal allocation scheme of medium-scale air defense firepower. The algorithm utilized the strategy of limiting the number of repetitive individuals in the population, the cross mutation strategy of individuals with similar fitness, the deletion strategy of partial optimal solution when falling into local extremum, and the transfer strategy of the current optimal solution in the chain link. Combining the advantages of multi-population parallel search, the algorithm could speed up convergence speed, maintain the diversity of population, and avoid falling into local extremum. In the simulation of standard test function and the application to air defense firepower task allocation problem, the proposed algorithm was compared with several typical optimization algorithms. Results show that the CMPGA algorithm had advantageous performance and could quickly find the optimal solution with high probability, which indicates the effectiveness and superiority of the algorithm.