Complex combinatorial optimization problems can be solved using the genetic algorithm, but there are also two shortcomings, one is lower search efficiency than other optimization algorithms, the other is easy to premature convergence and fall into local optimum. Therefore, this paper proposes the chaos “micro variation” genetic algorithm. Due to the characteristics of randomness and ergodicity, the chaos optimization algorithm solves the premature problem that genetic algorithm is easy to fall into the local optimal solution, which makes the proposed algorithm have strong local search ability and the ability to complete the global search for the optimal solution. At the same time, chaos disturbance is added into the selection operator of the genetic algorithm, the crossover operator and mutation operator are adjusted adaptively, and the fitness function is improved, such that the overall performance of the genetic algorithm is improved. Finally, the chaotic“micro mutation” genetic algorithm has faster evolution speed and higher search accuracy than the general chaotic genetic algorithm and classical genetic algorithm through the classical function verification.