In order to solve the problem of difficulty in achiving a higher material removal rate and a better surface integrity simultaneously, the highly nonlinear relationship between process parameters and machining performance was studied. A Taguchi experiment was designed with water pressure, pulseon time, pulseoff time, peak current and feed rate as the main optimization parameters, surface roughness(Ra) and material removal rate(MRR) as the optimization targets. It was innovative to apply the support vector machines (SVR) and particle swarm algorithm (PSO) to acquire the optimized parameters combination by establishing a multiobjective model. The results show that the multiobjective optimization model is very efficient in increasing MRR and reducing Ra, by 32% and 25% respectively under same conditions of Ra and MRR.