A dynamic source mask optimization (SMO) method is developed. The dynamic SMO method uses a dynamic fitness function in genetic algorithm to simulate the process variations in real lithography process. So the imaging quality of the optimized source and mask is not sensitive to the process errors. The dynamic SMO method can get similar result as the conventional weighted SMO method without the necessity of weighting coefficient optimization. Simulation results show that the dynamic method can get a usable defocus of 200 nm when the dose error is 15%. This is comparable with the optimized result of the weighted method. The dynamic SMO method can be also used to make the optimized source and mask less sensitive to other process errors, such as coma errors.