Municipal　solid　waste　incineration　(MSWI)　has　gradually　become　the　main　technology　of　waste　treatment　because　of　its　efficient　capacity　reduction.　However,　the　nitrogen　oxides　(NOx)　produced　in　the　MSWI　process　are　one　of　the　main　pollutants.　In　order　to　control　NOx　emissions　while　ensuring　combustion　efficiency,　an　intelligent　optimization　setting　method　of　air　flow　for　MSWI　process　based　on　multi-objective　particle　swarm　optimization　is　proposed.　Firstly,　by　the　combined　minimal-redundancy　maximal-relevance　criterion　and　the　feedforward　neural　network,　the　prediction　models　of　combustion　efficiency　and　NOx　emission　are　established.　Then,　an　improved　staged　multi-objective　particle　swarm　optimization　algorithm　(SMOPSO)　is　presented　to　obtain　the　Pareto　optimal　solutions　of　primary　air　flow　and　secondary　air　flow.　In　addition,　the　utility　function　is　designed　to　determine　the　optimal　setting　value　of　the　primary　air　flow　and　the　secondary　air　flow.　Finally,　the　simulation　experiments　verify　the　validity　and　feasibility　of　the　proposed　method　based　on　the　practical　operation　data.　©　2023　Northeast　University.　All　rights　reserved.