In this study, a CFD-based ship hydrodynamic optimization tool has been further developed by integrating the response surface method into the optimization module. Specifically, one of the response surface models, Kriging model, is employed to represent the relationship between the objective functions (output) and design variables (input) using stochastic process in the CFD-based hydrodynamic optimization of hull forms. To extract design knowledge for a hull form configuration, a data mining technique is applied. Functional analysis of variance (ANOVA), which can show the interaction between each design variable and objective functions, is utilized. Based on the information from ANOVA, the influence of design variables on the objective functions can be easily analyzed. For the purpose of illustration, the present hydrodynamics optimization tool has been used in a hull form optimization application, where the Series-60 hull form is taken as an initial hull form and the hull form is optimized by minimizing the wave drag at a given speed range. Numerical results show that the present response surface method can greatly reduce the computational cost associated with the CFD runs.