In　recent　years,　bacterial　foraging　optimization　(BFO)　has　been　used　to　solve　multiobjective　optimization　problems　(MOPs).　However,　BFO　has　not　fully　developed　its　potentials　on　MOPs　for　the　reason　of　lacking　of　in-depth　research　on　the　optimization　mechanisms　and　the　diversity　maintenance　strategies.　To　solve　it,　this　paper　develops　a　multi-resolution　grid-based　BFO　algorithm　(called　as　MRBFO).　MRBFO　redesigns　four　tailored　optimization　mechanisms　for　MOPs　including　chemotaxis,　conjugation,　reproduction,　and　elimination　and　dispersal　to　search　optimal　nondominated　solutions.　Moreover,　MRBFO　defines　a　multi-resolution　grid　strategy　to　produce　well-distributed　diverse　nondominated　solutions.　The　performance　of　MRBFO　is　comprehensively　evaluated　by　comparing　it　with　several　state-of-the-art　algorithms　on　many　benchmark　test　problems.　The　empirical　results　have　sufficiently　verified　the　advantages　of　MRBFO.