To　reduce　the　amount　of　energy　consumed　in　wastewater　treatment　plants,　nine　methods　were　used　to　select　the　key　operation　parameters　that　affected　energy　consumption　according　to　daily　operation　records,　and　an　intelligent　operation　management　system　based　on　a　genetic　algorithm　was　constructed　by　mapping　the　relationships　between　energy　consumption　and　the　key　operation　parameters.　The　results　showed　that　the　prediction　and　management　of　energy　consumption　could　be　achieved　by　incorporating　the　strengthened　elastic　genetic　algorithm　into　the　extreme　gradient　boosting　model.　The　main　parameters　affecting　energy　consumption　were　the　influent　flow　rate,　effluent　total　nitrogen,　NH4+-N　loading　rate,　etc.,　and　the　energy　consumption　could　be　reduced　by　13-27%　(with　an　average　of　22%).　The　parameters　were　all　selected　from　the　daily　operation　records　of　the　wastewater　treatment　plant,　and　no　additional　complex　data　acquisition　system　was　needed　to　collect　specific　parameters.　This　study　provided　a　cost-effective　strategy　to　reduce　energy　consumption　in　wastewater　treatment　plants.