Set-point　optimization　of　wastewater　treatment　process　(WWTP)　is　critical　for　energy　savings　but　is　challenging　due　to　complex　nonlinear　mechanisms　and　measurement　noises.　To　address　this　optimization　problem,　a　mechanism-data-driven　multiobjective　optimization　method　is　developed　to　alleviate　deficiencies　in　mechanisms　and　process　data.　First,　a　mechanism-data-driven　model　is　established　to　describe　the　relationships　between　effluent　quality,　energy　consumption,　and　key　process　variables.　Then,　the　mechanisms　and　process　data　can　be　collaboratively　leveraged　to　alleviate　the　inaccuracy　of　mechanism　models　and　suppress　measurement　noises.　Second,　a　weighted　indicator-based　multiobjective　particle　swarm　optimization　algorithm　is　proposed　to　suppress　uncertainties　introduced　by　measurement　noises.　Then,　the　set-points　with　noise　robustness　are　obtained　to　improve　optimization　performance　under　real　restricted　conditions.　Third,　the　proposed　method　is　applied　to　the　benchmark　simulation　model　No.　1　to　evaluate　its　capability.　The　results　demonstrate　that　this　method　can　improve　the　optimization　performance　of　WWTP.