Image　segmentation　is　an　important　task　in　image　analysis　and　processing.　Many　of　the　existing　methods　for　segmenting　a　multi-component　image　(satellite　or　aerial)　are　very　slow　and　require　a　priori　knowledge　of　the　image　that　could　be　difficult　to　obtain.　Furthermore,　the　success　of　each　of　these　methods　depends　on　several　factors,　such　as　the　characteristics　of　the　acquired　image,　resolution　limitations,　intensity　in-homogeneities　and　the　percentage　of　imperfections　induced　by　the　process　of　image　acquisition.　Evolutionary　programming(EP)　has　been　applied　with　success　to　many　numerical　and　combinatorial　optimization　problems　in　recent　years.　EP　has　rather　slow　convergence　rates,　however,　on　some　function　optimization　problems.　In　this　paper　the　new　evolutionary　programming　is　proposed　to　overcome　the　premature　convergence.　There　are　two　step　mutation　in　the　new　evolutionary　programming.　The　first　step　is　responsible　for　searching　the　whole　space.　The　second　is　responsible　for　searching　the　local　part　in　detail.　The　cooperation　and　specialization　between　different　two　step　mutation　are　considered　during　the　algorithm　design.　The　new　evolutionary　programming　can　use　in　image　segmentation　and　the　experimental　results　show　the　new　evolutionary　programming　is　efficient.