Individualized treatment rules aim to identify if, when, which, and to whom treatment should be applied. One untackled question is the inference of the optimal individualized treatment rule. We propose a procedure for the simultaneous estimation and inference of such treatment rule with the existence of high dimensional covariates.The estimation procedure estimates the optimal individualized treatment rule as a weighted classification problem, while enjoying double robustness property. The inference procedure utilizes the data splitting, data pooling, and the semiparametric de-correlated score to conquer the slow convergence rate of estimated outcome regression or propensity score. The asymptotic properties for this procedure and its extensions are investigated via an analysis combining the techniques in high dimensional data and the doubly robustness. Simulation and real data analysis are conducted to justify the superiority of the proposed estimation and inference procedure under various settings.