Determining the correct number of layers as input for 1D resistivity inversion is important for constructing a model that well represents the subsurface. In most electrical resistivity inversions, the number of layers is an arbitrary user-defined parameter, or it is determined through trialand-error by running the inversion many times using different numbers of layers and choosing the number of layers that produces the best model-data fit. Here, we provide a method that solves the problem of choosing the correct number of layers. The method follows the twosteps approach suggested by Simms and Morgan (1992) to systematically resolve the optimum number of layers. The first step is to run a fixed layer thickness inversion. Then, we use the outcome of the first inversion to determine the optimum number of layers as an input parameter for the second step which is a variable-thickness inversion (layer thicknesses and resistivities are inversion parameters) for the final resistivity model. Both steps use rescaled Ridge Trace least square regressions. The computer program for this method determines other the input parameters from the data file. The method utilizes an integrated program that performs the two inversion steps sequentially. The proposed method, which uses the robust Ridge Trace regression algorithm, has proven to be stable and accurate.