As the critical dimension in integrated circuit fabrication moving toward nodes-2Xnm and below, source and mask optimization (SMO) has been one of the most effective solutions of resolution enhancement techniques (RETs) to extend the process window and decrease process factor of 193 nm ArF lithography. We propose an efficient SMO method based on stochastic parallel gradient descent (SPGD) algorithm. The gradients of the objective function are estimated by random disturbance and utilized to guide the optimization, which avoids to calculate the analytic expression of the gradients. The complexity of optimization is reduced. The proposed SMO method is demonstrated using three mask patterns, including a periodic array of contact holes, a cross gate and dense lines. Three kinds of mask pattern error (PE) are reduced by 75%, 80% and 70% respectively. The numerical results show that our method can provide great improvements in printed image quality.