Computer-aided Molecular and Process Design, CAMPD, is a technique that simultaneously optimizes the choice of materials, such as solvents, and the corresponding process configurations for many chemical separation processes. The technique involves formulating an equation-oriented optimization model representing the overall design problem, which then can be solved in many ways depending on the chemicals involved, the property and process models, and the complexity and size of the problem, among others. Due to the complexity and large-size of the problem, and a lack of predictive property models, we have applied a decomposition-based CAMPD strategy that involves solving a series of subproblems sequentially to reduce the overall search space, thereby reducing the computational burden. We illustrate our strategy through a case study involving the design of ionic liquids (ILs) as solvents for the extractive-distillation based separation of an azeotropic refrigerant mixture, R-410A. Separation of such mixtures is gaining increased interest due to the need to remove, substitute or reuse constituent refrigerant chemicals that have undesirable properties (such as high global warming potential, flammability, etc.). ILs are considered because of their designable properties as functions of their molecular structures. Based on available measured data, group-contribution based predictive property models have been developed and interfaced with the workflow of the proposed strategy. A set of promising ILs have been identified and their performance verified through process simulation.