Switching time optimization has been an important issue for various types of optimal control problems. In the traditional control parameterization approach, the control is approximated by a piecewise constant function, whose heights are decision variables to be optimized. The switching times are typically equidistant, with no flexibility to adaptively optimize their values. Thus, to obtain more accurate results, it is usually necessary to choose a very fine partition of the time horizon. Consequently, the finite-dimensional approximate optimization problem will consist of a large number of decision variables, which leads to a large optimal parameter select problem. In this talk, we will introduce two techniques for optimizing control switching time - direction optimization and time scaling transformation to reduce the number of subintervals required.