The　municipal　solid　waste　incineration　(MSWI)　process　has　become　the　primary　technology　for　municipal　solid　waste　(MSW)　treatment　worldwide　due　to　its　advantages　of　harmlessness,　reduction,　and　resource　recovery.　However,　the　automatic　combustion　control　(ACC)　system　developed　in　developed　countries　are　often　not　effectively　applicable　to　other　countries,　particularly　China,　due　to　regional　differences　in　MSW　composition.　To　achieve　intelligent　development　of　MSWI　plants,　it　is　crucial　to　develop　intelligent　optimal　control　technology　tailored　to　national　conditions　to　reduce　comprehensive　pollutant　emission　concentration　(CPEC)　and　improve　combustion　efficiency　(CE).　This　article　proposes　a　data-driven　multi-objective　intelligent　optimal　control　strategy.　Firstly,　based　on　the　analysis　of　the　whole　process,　the　Tikhonov　regularization-least　regression　decision　tree　(TR-LRDT)　algorithm　is　used　to　establish　a　MSWI　whole　process　model,　incorporating　serial　controlled　objects　and　parallel　pollutant　indicators,　to　support　a　multi-objective　optimization　model.　Then,　leveraging　the　ACC　system　and　expert　experience,　a　multi-input　multi-output　loop　controller　is　established　using　the　single　neuron　adaptive　PID　(SNA-PID)　algorithm　to　achieve　stable　control　of　key　controlled　variables.　Next,　a　mutation　crossover　strategy　and　termination　condition　are　integrated　with　the　multi-objective　particle　swarm　optimization　(MOPSO)　algorithm　to　solve　the　multi-objective　optimization　model,　and　domain　expert　rules　are　applied　to　determine　the　optimal　setpoints　for　key　controlled　variables　in　the　Pareto　front,　aiming　to　reduce　CPEC　and　improve　CE.　Finally,　the　effectiveness　of　the　proposed　intelligent　optimal　control　strategy　is　validated　using　actual　data.　The　experimental　results　demonstrate　that　this　strategy　not　only　maintains　the　stability　of　key　controlled　variables　but　also　decreases　CPEC　by　1.82%　and　increases　CE　by　2.38%,　laying　a　foundation　for　further　research　into　intelligent　optimization　control　of　the　MSWI　process.　Further,　the　software　system　based　on　the　proposed　strategy　is　developed　and　validated　on　a　hardware-in-loop　simulation　platform.