In order to solve fire distribution optimization problem, quantum immune genetic algorithm model is built in the paper. Immune genetic algorithm is introduced to the quantum genetic algorithm to enhance the precision and the stability of the quantum genetic algorithm, which includes the mechanism of immunological memory and immunologic and keeps the balance between quantum genetic algoritm and immune genetic algorithm, It can improve its property by using priori knowledge and local information from the process of solving problem. For illustration, a fire distribution optimization example is utilized to show the feasibility of the quantum immune genetic algorithm model in solving fire distribution optimization problem. Compared with other evolution algorithms, empirical results show that the quantum immune genetic algorithm possesses the characters such as higher velocity of convergence and better optimization seeking. It is proved that quantum immune genetic algorithm is more effective than other intellect algorithms in solving optimization of fire distribution by simulation experiment in the paper.