A two-step method for the solution of dynamic optimization problems with state inequality constraints using iterative dynamic programming (IDP) is presented. In the first step, a preliminary control policy along with approximate values for the start and end points of the state-constrained arc is obtained. The final solution is obtained in the second step by using the preliminary control policy as an input along with an analytical expression for the control during the constrained portion of the state trajectory. The use of flexible stage lengths in IDP helps in accurately locating the start and end points of the state-constrained arc. For the examples considered, the method results in better values of the performance index than those previously reported in the literature. It also results in a significant reduction in computation time as compared to the existing method for the solution of problems with a single active state constraint and a single control using IDP.