A　dynamic　multi-objective　optimization　control　(DMOOC)　scheme　is　proposed　in　this　paper　for　the　wastewater　treatment　process　(WWTP),　which　can　dynamically　optimize　the　set-points　of　dissolved　oxygen　concentration　and　nitrate　level　with　multiple　performance　indexes　simultaneously.　To　overcome　the　difficulty　of　establishing　multi-objective　optimization　(MOO)　model　for　the　WWTP,　a　neural　network　online　modeling　method　is　proposed,　requiring　only　the　process　data　of　the　plant.　Then,　the　constructed　MOO　model　with　constraints　is　solved　based　on　the　NSGA-II　(non-dominated　sorting　genetic　algorithm-II),　and　the　optimal　set-point　vector　is　selected　from　the　Pareto　set　using　the　defined　utility　function.　Simulation　results,　based　on　the　benchmark　simulation　model　1　(BSM1),　demonstrate　that　the　energy　consumption　can　be　significantly　reduced　applying　the　DMOOC　than　the　default　PID　control　with　the　fixed　set-points.　Moreover,　a　tradeoff　between　energy　consumption　and　effluent　quality　index　can　be　considered.