Multi-objective　optimization　is　an　important　and　challenging　topic　in　the　field　of　industrial　design　and　scientific　research　because　real-world　problems　usually　involve　several　conflicting　objectives.　Since　a　multi-objective　evolutionary　algorithms　(MOEA)　is　able　to　obtain　an　approximation　to　the　Pareto　optimal　set　and　provide　substantial　information　of　the　tradeoff　between　objectives,　it　is　becoming　one　of　the　most　successful　methods　for　multi-objective　optimization.　Usually,　an　MOEA　generates　new　trial　solutions　(offspring)　with　some　candidate　decision　vectors　(parents)　to　search　for　the　promising　areas　and　make　the　population　evolve　towards　the　Pareto　optimal　set.　Moreover,　in　the　reproduction　procedure,　most　of　MOEAs　view　the　decision　vector　as　a　whole,　and　do　not　recognize　the　effects　of　a　single　component　on　the　new　trial　solutions.　In　this　paper,　we　propose　the　component-emphasizing　mechanism　for　enhancing　the　search　ability　of　MOE　As.　In　this　mechanism,　each　component　of　a　decision　vector　is　viewed　as　an　independent　factor　affecting　the　quality　of　the　solution.　Based　on　the　mechanism,　a　new　MOEA　is　presented.　Finally,　the　performance　of　this　new　algorithm　is　compared　with　two　other　promising　MOEAs,　namely,　NSGA-II　and　GDE3,　on　a　set　of　test　instances.　The　experimental　results　have　shown　that　the　proposed　algorithm　outperforms　the　others　in　solution　quality　and　time　cost.　©　2010　IEEE.