From　the　aspect　of　solution　space,　the　role　of　crossover　operators　is　analysed　firstly.　The　essence　of　crossover　operators　is　that　it　can　choose　values　at　random　from　the　solution　space　included　father　individuals.　So,　it　is　not　absolute　that　the　performance　of　offspring　individuals　is　better　than　that　of　father　individuals　after　the　father　individuals　crossed.　And　it　is　very　easy　to　bring　the　aimless　search.　An　improved　genetic　algorithm　based　on　oriented　crossover　is　proposed.　It　can　make　the　offspring　individuals　evolve　towards　the　target　value　by　optimizing　their　crossover　positions　and　the　evolving　probability　is　very　large.　The　simulation　results　show　the　algorithm　can　improve　greatly　the　efficiency　and　precision　in　finding　the　optimum　value.