Individualized treatment regimes aim to determine the optimal treatment decision based on patient-specific covariates to maximize the clinical benefit for the entire patient population. When the response variable is survival time, the presence of censoring in survival data complicates the direct estimation of the optimal ITR. We propose a novel consistent estimator for the optimal ITR in the context of censored data. Assuming the propensity score model is known, our method adjusts the censored data via a K-S-V transformation, which is essentially a weighting adjustment of the response variable. Subsequently, the optimal decision regimen is estimated by maximizing the concordance function and the value function, respectively. The performance of our proposed method is demonstrated through simulation studies and its application to the ACTG175 clinical trial dataset.