Sensitivity analysis (SA) is an important procedure in engineering design to obtain valuable information about the model behavior to guide a design process. For design under uncertainty, probabilistic sensitivity analysis (PSA) methods have been developed to provide insight into the probabilistic behavior of a model. In this paper, the goals of PSA at different design stages are investigated. In the prior-design stage, PSA can be utilized to identify those probabilistically non-significant variables and reduce the dimension of a random design space. It can reduce the computational cost associated with uncertainty assessment without much sacrifice on the optimum solution. For post-design analysis, probabilistic sensitivity analysis can be used to identify where to spend design resources for the largest potential improvement of a performance. Based on the interested distribution range of a random response, the PSA methods can be categorized into two types: the global response probabilistic sensitivity analysis (GRPSA) and the regional response probabilistic sensitivity analysis (RRPSA). Four widely-used PSA methods: Sobol' indices, Wu's sensitivity coefficients, the MPP based sensitivity coefficients, and the Kullback-Leibler entropy based method are selected for comparison. The merits behind each method are reviewed in details. Their advantages, limitations, and applicability are investigated. Their effectiveness and applicability under different design scenarios are compared in two numerical examples and two engineering design problems.