A　novel　method　of　constructing　the　cost-sensitive　decision　trees　based　on　multi-objective　optimization　is　proposed　in　this　paper.　The　average　misclassification　cost　and　the　average　test　cost　are　treated　as　the　two　optimization　objectives.　NNIA　(Nondominated　Neighbor　Immune　Algorithm)　is　exploited　to　optimize　the　decision　trees.　And　some　Pareto　decision　trees　are　finally　obtained.　Experimental　results　show　that,　compared　with　the　C4.5　algorithm　and　CSDB　(Cost　Sensitive　Decision　Tree)　algorithm,　the　proposed　method　in　this　paper　can　not　only　outperform　these　two　methods　in　terms　of　the　two　above　objectives　but　also　achieve　smaller　size　of　the　decision　trees　and　stronger　generalization　ability.