A　evolutionary　programming　is　proposed　in　this　paper　to　automatically　design　neural　networks(NNS)　ensembles.　Based　on　negative　correlation　learning,　different　individual　NNs　in　the　ensemble　can　learn　to　subdivide　the　task　and　thereby　solve　it　more　efficiently　and　elegantly.　At　the　same　time,　different　individual　NNs　are　always　to　find　the　best　collaboration　connection　during　the　evolutionary　process.　In　addition,　the　architecture　of　each　NN　in　the　ensemble　and　the　size　of　the　ensemble　need　not　to　be　predefined.　The　Neural　Networks　Ensembles　based　on　evolutionary　programming　is　designed　in　order　to　solve　Job　Shop　Schedule　Problem.　The　simulation　results　show　that　the　proposed　method　in　this　paper　is　valid.　©　2010　IEEE.