With　the　advent　of　the　computer　science,　the　data　volume　that　needed　to　be　processed　under　many　practical　situations　increases　dramatically,　challenging　many　traditional　machine　learning　techniques.　Bearing　this　in　mind,　we　made　an　intensive　study　on　the　optimization　of　decision　tree　algorithm　and　its　corresponding　porting　to　the　big　data　analysis　in　this　paper.　An　optimized　genetic　algorithm　is　merged　into　the　implementation　of　the　decision　tree　algorithm　above,　and　we　also　invent　a　parallel　genetic　decision　tree　algorithm　using　MapReduce,　which　is　very　suitable　for　analyzing　big　data　in　cloud　computing　environment.　Experiment　results　show　that　our　algorithm　acquires　a　nearly　linear　speedup,　keeping　a　similar　classification　accuracy　at　the　same　time.