In　this　paper,　for　the　operations　of　wastewater　treatment　processes　(WWTPs),　an　intelligent　multi-objective　optimization　control　(IMOOC),　based　on　an　adaptive　multi-objective　differential　evolution　(AMODE)　algorithm,　is　proposed　to　search　for　the　suitable　set-points　to　balance　the　treatment　performance　and　the　operational　costs.　In　this　IMOOC,　the　combination　of　an　AMODE　algorithm　and　the　multi-objective　critical　issues　helps　us　to　fulfill　all　the　control　objectives　simultaneously.　To　improve　the　optimization　efficiency　and　achieve　fast　convergence,　the　AMODE　algorithm　is　designed　to　improve　the　local　search　and　the　global　exploration　abilities:　The　adaptive　adjustment　strategies　are　developed　to　select　the　suitable　scaling　factor　and　crossover　rate　in　the　process　of　searching.　Meanwhile,　the　multi-objective　critical　issues,　according　to　the　state　of　the　processes,　are　given　as　a　nonlinear　multi-objective　optimization　problem　to　evaluate　the　operational　performance　of　WWTPs.　Therefore,　once　the　nonlinear　multi-objective　optimization　problem　is　solved　at　each　sampling　time,　the　most　appropriate　set　of　Pareto　is　selected　as　suitable　set-points　to　achieve　the　process　performance.　To　demonstrate　the　merits　of　our　proposed　method,　the　proposed　IMOOC　is　applied　to　the　Benchmark　Simulation　Model　No.　1　of　WWTPs.　The　results　show　that　the　proposed　IMOOC　effectively　provides　process　control.　The　performance　comparison　with　other　algorithms　also　indicates　that　the　proposed　optimal　strategy　yields　better　effluent　qualities　and　lower　average　operation　consumption.