It　is　challenging　to　introduce　an　optimization　strategy　for　enhancing　the　operational　performance　of　wastewater　treatment　process　(WWTP)　on　account　of　its　multi-task　characteristics.　To　cope　with　this　problem,　an　adaptive　multi-task　optimization　(AMTO)　strategy　is　proposed　to　realize　the　optimal　operation　of　WWTP.　First,　a　multi-task　optimization　(MTO)　framework　is　developed　to　depict　the　multi-task　characteristics　of　WWTP.　Then,　it　is　conducive　to　achieve　the　optimization　of　nitrogen　removal　and　phosphorus　removal　processes　simultaneously.　Second,　a　data-driven　MTO　model,　based　on　the　relevant　process　data,　is　established　to　describe　the　characteristics　of　nitrogen　removal　task　and　phosphorus　removal　task.　Then,　a　MTO　problem　is　modeled　for　WWTP.　Third,　a　multi-task　particle　swarm　optimization　algorithm,　based　on　the　adaptive　knowledge　transfer　method,　is　developed　to　cope　with　the　above　MTO　problem.　Then,　the　optimal　set-points　can　be　obtained　in　WWTP.　Finally,　the　effectiveness　of　the　proposed　AMTO　strategy　is　verified　by　comparing　with　other　optimization　strategies.　The　results　demonstrate　that　the　proposed　AMTO　strategy　can　achieve　multiple　tasks　optimization　in　parallel　and　the　optimal　operation　of　WWTP.(c)　2022　Elsevier　Ltd.　All　rights　reserved.