A　new　method　for　continuous　global　minimization　problems,　acronymed　SCM,　is　introduced.　This　method　gives　a　simple　transformation　to　convert　the　objective　function　to　an　auxiliary　function　with　gradually　＇fewer＇　local　minimizers.　All　Local　minimizers　except　a　prefixed　one　of　the　auxiliary　function　are　in　the　region　where　the　function　value　of　the　objective　function　is　lower　than　its　current　minimal　value.　Based　on　this　method,　an　algorithm　is　designed　which　uses　a　local　optimization　method　to　minimize　the　auxiliary　function　to　find　a　local　minimizer　at　which　the　value　of　the　objective　function　is　lower　than　its　current　minimal　value.　The　algorithm　converges　asymptotically　with　probability　one　to　a　global　minimizer　of　the　objective　function.　Numerical　experiments　on　a　set　of　standard　test　problems　with　several　problems＇　dimensions　up　to　50　show　that　the　algorithm　is　very　efficient　compared　with　other　global　optimization　methods.