Multiobjective　optimization　problems　widely　exist　in　engineering　application　and　science　research.　This　paper　presents　an　archive　bacterial　foraging　optimizer　to　deal　with　multiobjective　optimization　problems.　Under　the　concept　of　Pareto　dominance,　the　proposed　algorithm　uses　chemotaxis,　conjugation,　reproduction　and　elimination-and-dispersal　mechanisms　to　approximate　to　the　true　Pareto　fronts　in　multiobjective　optimization　problems.　In　the　optimization　process,　the　proposed　algorithm　incorporates　an　external　archive　to　save　the　nondominated　solutions　previously　found　and　utilizes　the　crowding　distance　to　maintain　the　diversity　of　the　obtained　nondominated　solutions.　The　proposed　algorithm　is　compared　with　two　state-of-the-art　algorithms　on　four　standard　test　problems.　The　experimental　results　indicate　that　our　approach　is　a　promising　algorithm　to　deal　with　multiobjective　optimization　problems.　©　Copyright　2016　by　SCITEPRESS　-　Science　and　Technology　Publications,　Lda.　All　rights　reserved.