This　paper　describes　an　importance　sampling　Monte　Carlo　(MC)　simulation　model　for　the　failure　probability　calculation　of　networks　with　high　reliability.　First,　an　evolution　process　algorithm　for　network　connectivity　judgment　with　imperfect　nodes　and　arcs　is　proposed.　Random　numbers　generated　in　each　simulation　were　transformed　into　repair　time　of　network　elements　(arcs　and　nodes)　according　to　the　elements＇　reliability.　The　network　connectivity　topology　is　constructed　by　the　order　of　repair　times,　which　is　treated　as　the　evolution　process　of　network　connectivity.　Next,　network　failure　probabilities　of　2\K\All　terminal　problems　are　calculated　by　importance　sampling　Monte　Carlo　(ISMC)　simulation.　The　importance　sampling　function　of　ISMC　is　evaluated　by　a　multi-criteria　iterative　method　based　on　the　evolution　process　algorithm　and　cross　entropy　model.　Finally,　the　proposed　model　is　illustrated　and　tested　by　benchmark　networks.　Testing　results　of　network　with　high　reliability　show　that　the　number　of　iterations　required　by　the　multi-criteria　iterative　method　is　about　one　fortieth　of　that　number　of　exist　method　when　solving　the　importance　sampling　function.　Therefore,　the　proposed　model　is　efficient　for　networks　models　with　high　reliability.　©　2016,　Editorial　Board　of　Journal　of　Systems　Engineering　Society　of　China.　All　right　reserved.