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In recent years, GPFs (Gasoline particulate filters) have been installed in gasoline engines to comply with stricter environmental regulations in China and Europe. In particular, coated-GPFs having a catalytic purification function are required to have high conversion performances, high filter efficiencies in the sense of a high collection efficiency, and low pressure loss. It is not easy to design a filter that satisfies all these parameters. Experimental studies are being conducted, but it is costly to study in trial productions. In this technical paper, a GPF design optimization method will be proposed that combines multi-scale simulation, surrogate models by machine learning, and an optimization algorithm. By using this method, a GPF design that minimizes pressure loss while providing high conversion performance and particle collection rates that satisfy current regulations can be created.