In view of the electrical discharge machining has the characteristics of complex, time-varying and multiparameter,based on the feasibility of the electrical discharge machining of the monocrystalline Silicon,this paper has a comprehensive evaluation of the material removal rate and the surface roughness and other technology goals. Using central composite design experiments,it comprehensively inspects the influence degree of the peak current、pulse width and pulse interval to the material removal rate and the surface roughness in the course of the electrical discharge machining of the P type monocrystalline silicon,it obtains the second-order response model of the material removal rate and surface roughness respectively through the Response Surface Methodology,the result of the variance analysis indicate that the model has good fitting degree and adaptability. Aiming at improving the material removal rate and reducing the surface roughness to establish the process parameters optimization model,it designs genetic algorithm for solving optimization problem and get the Pareto optimal solution set. The experimental results show that the average relative error of the experimental results of the material removal rate compare with the optimized forecast results is 5. 8%,the average relative error of the experimental results of the surface roughness compare with the optimized forecast results is 5. 3%,which is showing that this algorithm can rapidly and efficiently implement the optimization of the process parameters in the course of the electrical discharge machining of the P type monocrystalline silicon.