This　paper　focuses　on　an　energy-efficient　job-shop　scheduling　problem　within　a　machine　speed　scaling　framework,　where　productivity　is　affected　by　deterioration.　To　alleviate　the　deterioration　effect,　necessary　maintenance　activities　must　be　put　in　place　during　the　scheduling　process.　In　addition　to　sequencing　operations　on　machines,　the　problem　at　hand　aims　to　determine　the　appropriate　speeds　of　machines　and　positions　of　maintenance　activities　for　the　schedule,　in　order　to　minimise　the　total　weighted　tardiness　and　total　energy　consumption　simultaneously.　To　deal　with　this　problem,　a　multi-population,　multi-objective　memetic　algorithm　is　proposed,　in　which　the　solutions　are　distributed　into　sub-populations.　Besides　a　general　local　search,　an　advanced　objective-oriented　local　search　is　also　executed　periodically　on　a　portion　of　the　population.　These　local　search　methods　are　designed　based　on　a　new　disjunctive　graph　introduced　to　cover　the　solution　space.　Furthermore,　an　efficient　non-dominated　sorting　method　for　bi-objective　optimisation　is　developed.　The　performance　of　the　memetic　algorithm　is　evaluated　via　a　series　of　comprehensive　computational　experiments,　comparing　it　with　state-of-the-art　algorithms　presented　for　job-shop　scheduling　problems　with/without　considering　energy　efficiency.　Experimental　results　confirm　that　the　proposed　algorithm　can　outperform　other　algorithms　being　compared　across　a　range　of　performance　metrics.　©　2020