Most　of　the　existing　methods　for　belief　rule　based　(BRB)　focus　on　single　objective　optimization　for　parameter　or　structure.　However,　according　to　the　existing　research,　improving　reasoning　accuracy　and　reducing　the　complexity　of　BRB　system　usually　conflict　each　other.　Thus,　designing　a　suitable　algorithm　to　find　right　trade-off　for　the　two　goals　is　necessary.　For　this　purpose,　an　algorithm　named　M-PAES-BRB　(belief　rule　base　inference　method　based　on　multi-objective　optimization)　is　proposed　to　determine　an　approximation　of　the　optimal　Pareto　front　by　concurrently　minimizing　the　root　mean　squared　error　and　the　complexity.　The　algorithm　adopts　an　improved　mixed　pareto　archived　evolutionary　strategy　(M-PAES)　to　build　a　multi-objective　optimization　model,　M-PAES　use　recombination　operator　and　mutation　operator　to　generate　candidate　solutions.　In　the　experiment,　we　select　two　standard　time　series,　Mackey-Glass　and　Box-Jenkins　as　the　experimental　datasets,　to　test　the　feasibility　and　effectiveness　of　M-PAES-BRB.　Compared　to　fuzzy　rule　base　multi-objective　evolutionary　algorithms　(FRBSs),　the　experiment　results　show　that　M-PAES-BRB＇s　reasoning　accuracy　is　higher　and　the　complexity　is　lower.　©　2019,　Editorial　Board　of　Journal　of　the　University　of　Electronic　Science　and　Technology　of　China.　All　right　reserved.