The subclassification based on the propensity score is a substantial inference procedure for estimating average causal effects in non-randomized studies. One of the practical problems is optimally determining the number of subclasses and their cutpoints. Ideally, 1) variation, 2) predictivity to the treatment assignment and,3) association with the outcome of the propensity scores needs to be optimally little in each subclass. Further, 4) the optimal balance of assignments in each subclass is needed. From their perspectives; we proposed a new methodology on propensity scores subclassification using graphical presentations [indexes], such as receiver operating characteristic (ROC) curve [area under the curve (AUC)] and Lorenz curve [Gini index]. Researchers can visually check the properties, such as the positivity assumption, to be satisfied with a particular subclass, which leads to an accurate estimate of the causal effect. We conduct simulations to compare with quantile method, and an application to our proposed method, based on right heart catheterization dataset, is provided.