Evolutionary　Algorithm　(EA)　is　a　population-based　metaheuristic　technique　to　effectively　solve　Multiobjective　Optimization　Problem　(MOP).　However,　it　is　still　an　active　research　topic　how　to　improve　the　performance　of　MOEA　algorithms.　In　this　paper,　we　present　a　new　FOPF　algorithm,　which　can　alleviate　MOEA＇s　disadvantage　on　time　performance.　First,　a　fast　obtaining　Pareto　front　approach　with　less　computation　cost　is　proposed,　then　an　expand　approach　and　a　limited　crossover　procedure　are　employed　to　keep　the　diversity　of　solutions.　Experimental　results　on　four　test　problems　show　that　the　FOPF　algorithm　is　able　to　find　solutions　with　good　diversity,　which　are　near　the　true　Parato-optimal　front,　and　improves　significantly　time　performance　compared　to　the　known　NSGA2.　©　2009　IEEE.