To　quickly　and　precisely　extract　the　parameters　for　solar　cell　models,　inspired　by　simplified　bird　mating　optimizer　(SBMO),　a　new　optimization　technology　referred　to　as　population　classification　evolution　(PCE)　is　proposed.　PCE　divides　the　population　into　two　groups,　elite　and　ordinary,　to　reach　a　better　compromise　between　exploitation　and　exploration.　For　the　evolution　of　elite　individuals,　we　adopt　the　idea　of　parthenogenesis　in　nature　to　afford　a　fast　exploitation.　For　the　evolution　of　ordinary　individuals,　we　adopt　an　effective　differential　evolution　strategy　and　a　random　movement　of　small　probability　is　added　to　strengthen　the　ability　to　jump　out　of　a　local　optimum,　which　affords　a　fast　exploration.　The　proposed　PCE　is　first　estimated　on　13　classic　benchmark　functions.　The　experimental　results　demonstrate　that　PCE　yields　the　best　results　on　11　functions　by　comparing　it　with　six　evolutional　algorithms.　Then,　PCE　is　applied　to　extract　the　parameters　for　solar　cell　models,　that　is,　the　single　diode　and　the　double　diode.　The　experimental　analyses　demonstrate　that　the　proposed　PCE　is　superior　when　comparing　it　with　other　optimization　algorithms　for　parameter　identification.　Moreover,　PCE　is　tested　using　three　different　sources　of　data　with　good　accuracy.