A　Pareto-based　multi-objective　optimization　approach　is　proposed　to　design　multichannel　FBG　filters.　Instead　of　defining　a　single　optimal　objective,　the　proposed　method　establishes　the　multi-objective　model　by　taking　two　design　objectives　into　account,　which　are　minimizing　the　maximum　index　modulation　and　minimizing　the　mean　dispersion　error.　To　address　this　optimization　problem,　we　develop　a　two-stage　evolutionary　computation　approach　integrating　an　elitist　non-dominated　sorting　genetic　algorithm　(NSGA-II)　and　technique　for　order　preference　by　similarity　to　ideal　solution　(TOPSIS).　NSGA-ll　is　utilized　to　search　for　the　candidate　solutions　in　terms　of　both　objectives.　The　obtained　results　are　provided　as　Paretic)　front.　Subsequently,　the　best　compromise　solution　is　determined　by　the　TOPSIS　method　from　the　Paretic)　front　according　to　the　decision　maker＇s　preference.　The　design　results　show　that　the　proposed　approach　yields　a　remarkable　reduction　of　the　maximum　index　modulation　and　the　performance　of　dispersion　spectra　of　the　designed　filter　can　be　optimized　simultaneously.　(C)　2015　Elsevier　B.V.　All　rights　reserved