Reliability worth analysis is an important tool for distribution systems planning and operations. The interruption cost model used in the analysis directly affects the accuracy of the reliability worth evaluation. In this dissertation, the reliability worth analysis was dealt with two interruption cost models including an average or aggregated model (AAM), and a probabilistic distribution model (PDM) in two phases. In the first phase, the dissertation presents a reliability cost model based AAM for distribution system planning. The reliability cost model has been derived as a linear function of line flows for evaluating the outages. The objective is to minimize the total cost including the outage cost, feeder resistive loss, and fixed investment cost. The Evolutionary Programming (EP) was used to solve the very complicated mixed-integer, highly non-linear, and non-differential problem. A real distribution network was modeled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process. In the second phase, the interruption cost model PDM was proposed by using the radial basis function (RBF) neural network with orthogonal least-squares (OLS) learning method. The residential and industrial interruption costs in PDM were integrated by the proposed neural network technique. A Monte-Carlo time sequential simulation technique was adopted for worth assessment. The technique is tested by evaluating the reliability worth of a Taipower system for the installation of disconnected switches, lateral fuses, transformers and alternative supplies. The results show that the two cost models result in very different interruption costs, and PDM may be more realistic in modeling the system.