In　this　paper,　we　propose　a　new　Multi-Population　QUasi-Affine　TRansformation　Evolution　(MP-QUATRE)　algorithm　for　global　optimization.　The　proposed　MP-QUATRE　algorithm　divides　the　population　into　three　sub-populations　with　a　sort　strategy　to　maintain　population　diversities,　and　each　sub-population　adopts　a　different　mutation　scheme　to　make　a　good　balance　between　exploration　and　exploitation　capability.　In　the　experiments,　we　compare　the　proposed　algorithm　with　DE　algorithm　and　QUATRE　algorithm　on　CEC2013　test　suite　for　real-parameter　optimization.　The　experimental　results　indicate　that　the　proposed　MP-QUATRE　algorithm　has　a　better　performance　than　the　competing　algorithms.　©　2019,　Springer　Nature　Singapore　Pte　Ltd.