Increasing demand in therapeutic drugs has resulted in the need to design cost-effective, flexible and robust manufacturing processes capable of meeting regulatory product purity requirements. To facilitate this design procedure, a framework linking an evolutionary multiobjective algorithm (EMOA) with a biomanufacturing process economics model is presented. The EMOA is tuned to discover sequences of chromatographic purification steps, and equipment sizing strategies adopted at each step, that provide the best trade-off with respect to multiple objectives including cost of goods per gram (COG/g), robustness in COG/g, and impurity removal capabilities. The framework also simulates and optimizes subject to various process uncertainties and design constraints. Experiments on an industrially-relevant case study showed that the EMOA is able to discover purification processes that outperform the industrial standard, and revealed several interesting trade-offs between the objectives.