The　increasing　demand　for　wastewater　treatment　processes　is　to　improve　the　effluent　quality　and　reduce　the　energy　consumption.　However,　due　to　the　existence　of　different　time　scales　data　acquisition　for　effluent　quality　and　energy　consumption,　it　is　difficult　to　achieve　optimum　operation　of　wastewater　treatment　processes　with　multitime-scale　property.　To　solve　this　problem,　a　knowledge-data　driven　multitime-scale　optimal　control　(KDD-MTSOC)　is　designed　in　this　article.　First,　a　knowledge-data　driven　optimal　control　system　is　established　by　dividing　the　optimal　control　problem　into　different　time　scales,　and　solving　it　by　matching　with　appropriate　optimization　algorithms.　Then,　the　frequency　of　optimal　control　is　improved　and　data　is　fully　and　reasonably　used.　Second,　a　knowledge-based　regression　kernel　strategy　is　employed　to　establish　the　reasonable　objective　functions　and　constraints.　Then,　the　objective　functions　and　constraints　are　favorable　to　balance　performance　indexes　and　describe　the　multitime-scale　characteristics.　Third,　a　knowledge　decision-based　particle　swarm　optimization　(KDPSO)　algorithm　is　presented　to　solve　the　multitime-scale　optimization　problem　of　KDD-MTSOC.　Then,　the　KDPSO　algorithm　can　effectively　improve　the　operational　performance.　Finally,　the　proposed　KDD-MTSOC　is　applied　to　the　Benchmark　Simulation　Model　No.　1　to　verify　its　effectiveness.　The　experimental　results　demonstrate　that　the　KDD-MTSOC　method　can　achieve　outstanding　operational　performance.