A hybrid optimization strategy genetic algorithm is put forward to solve the problem of blending electrolyzer and aluminum scheduling in the electrolytic aluminum production process based on the mathematical model analysis. Artificial experience is used in order to rule out special case. The fitness function is optimized by the path of aluminum scheduling. The crossover operators are designed to implement the transformation of electrolyzer combination and the mutation operators are designed to change the route of loading. The effectiveness for the proposed method is demonstrated by 3 optimization examples of electrolyzer from a northwest aluminum plant of China.