In clinical practice, researchers usually categorize continuous variables for risk assessment. Many algorithms have been developed to find one optimal cut point to group variables into two halves; however, there is often need to determine the optimal number of cut points and their locations at the same time. In this proposal we propose a new AIC criterion, where the AIC values are corrected with cross-validation and Monte Carlo method, to select the optimal number of cut points. In addition, the cross-validation and Monte Carlo methods will be used to correct the p-value and relative risk. To provide the biomedical researchers with an easy tool, we aim to develop an R function that utilized the genetic algorithm to find the location of the optimal cut points. In addition, we plan to conduct simulations to study the performance of our proposed method. In the end, we will apply our method to study the effect of body mass index (BMI) on cervical cancer survival, which had inconsistent reports in the literature.