As a discrete algorithm to solve nonlinear optimization problems, iterative dynamic programming(IDP) algorithm is rather vulnerable to the stage of time in several aspects such as accuracy as well as the convergence rate. Traditionally, the time division associated with IDP algorithm relies on human’s subjective experiences, lacking effective guidance. Motivated by this observation and targeted at fixed terimal time optimizaton problem, a self-adaptive variable-step IDP algorithm is introduced in this paper, which can adjust the number, length and switching point of the time stages taking account of the performance and control variables, in order to improve the performance of IDP. The approach is applied to batch process optimization simulations. The results show that the time stages can be self-adjusted and the optimization performance can be improved.