This　paper　develops　a　novel　approach　for　automatic　parameter　calibration　of　soil　constitutive　models　by　combining　Bayesian　optimization　(BO)　and　genetic　algorithm　(GA).　The　BO　method　enables　locating　the　possible　point　over　a　large　multi-parameter　search　domain　quickly,　by　which　the　search　domain　is　no　longer　required　to　be　accurately　pre-defined.　Thereafter,　the　GA　is　employed　to　find　the　best　solution　within　the　BO-optimized　search　space.　This　calibration　procedure　can　quickly　give　the　best-matched　model　parameters　for　both　sands　and　clays　even　with　the　same　large　search　space.　Taking　a　critical　state-based　soil　model　(Clay　And　Sand　Model　(CASM))　as　an　example,　the　superiority　and　capability　of　the　proposed　calibration　method　are　examined　by　comparing　experimental　data　with　predicted　results　for　both　clays　and　sands.　It　is　shown　that　the　BO-GA-based　approach　can　reduce　errors　by　46.4%　for　clays　and　41.2%　for　sands　compared　with　the　conventional　GA　method　and　outperforms　other　traditional　optimization　algorithms.　Eventually,　the　evolution　process　of　BO　and　GA　is　visualized　to　better　understand　the　principles　of　automatic　parameter　calibration,　which　is　followed　by　model　uncertainty　analyses.　©　2024　Elsevier　Ltd