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A multistag​e process consists of sequential consecutive stages. In this process, each stage has multiple responses and is affected by its preceding stage, while at the same time, affecting the following stage. This complex structure makes it difficult to optimize the multistage process. Recently, it became easy to obtain a large amount of operational data from the multistage process due to development of information technologies. The proposed method employs a data mining method called a classification and regression tree for analyzing the data and desirability functions for simultaneously optimizing the multiresponse. To consider the relationship between stages, a backward optimization procedure which treats the multiresponse of the preceding stage as the input variables is proposed. The proposed method is described using a steel manufacturing process example and is compared with existing multiresponse optimization methods. The case study shows that the proposed method works well and outperforms the existing methods.