Abstract: For providing the optimizaiton performance of the genetic algorithm, the fuzzy dynamic regulator for mutation and crossover operators is constructed, the adjusting process of parameter is specified, and the implementing tactics as well as the controlling process are also provided in detail. The performances of the improved genetic algorithm and simple genetic algorithm are compared by the standard Benchmark testing function, the result indicate that the proposed method has many advantages such as higher optimization accuracy, higher optimization efficiency and fewer evolution steps.