In this study, we proposed a multiscale workflow that combines machine learning, NSGA, and process modeling to design MOFs with excellent process-level performance for post-combustion carbon capture. Initially, 247 optimal MOFs were tested, followed by the generation of 359 MOFs through constraint optimization to exclude oxalate groups and nitrogen/phosphorus-containing linkers. When compared to the existing benchmarks, most of our structures outperform the reference 13X zeolites in the NIST / APRA-E database .