Exemplar-based　clustering　algorithm　is　very　efficient　to　handle　large　scale　and　high　dimensional　data,　while　it　does　not　require　the　user　to　specify　many　parameters.　For　current　algorithms,　however,　are　the　inabilities　to　identify　the　optimal　results　or　specify　the　number　of　clusters　automatically.　To　remedy　these,　in　this　work,　we　propose　and　explore　the　idea　of　exemplar-based　clustering　analysis　optimized　by　genetic　algorithms,　abbreviated　as　ECGA　framework,　which　use　genetic　algorithms　for　optimizing　and　combining　the　results.　First,　an　exemplar-based　clustering　framework　based　on　canonical　genetic　algorithm　is　introduced.　Then　the　framework　is　optimized　with　three　new　genetic　operators:　(1)　Geometry　operator　which　limits　the　typology　distribution　of　exemplars　based　on　pair-wise　distances,　(2)　EM　operator　which　apply　EM　(Expectation　maximization)　algorithm　to　generate　children　from　previous　population　and　(3)　Vertex　substitution　operator　which　is　initialized　with　genetic　algorithm　and　select　exemplars　by　using　the　variable　neighborhood　search　meta-heuristic　framework.　Theoretical　analysis　proves　the　ECGA　can　achieve　better　chance　to　find　the　optimal　clustering　results.　Experimental　results　on　several　synthetic　and　real　data　sets　show　our　ECGA　provide　comparable　or　better　results　at　the　cost　of　slightly　longer　CPU　time.