Calibration aims to minimize uncertainty in the estimation by ensuring the accuracy of the estimation procedure. The auxiliary information is usually utilized to improve the accuracy of the estimation procedure under the calibration approach. In the present article, the information on two auxiliary variables is utilized to consider a new calibration estimator for the mean of a stratified population. A set of new calibration weights is obtained by minimizing the chi-square type distance function and subject to some calibration constraints based on the auxiliary information. The Taylor linearization technique is used to obtain the expression for the variance of the proposed calibration estimator. The effectiveness of the proposed calibration estimator has been demonstrated through a simulated study. A numerical example using the real data set has also been provided to support the theoretical findings. The analysis shows that the suggested calibration estimator outperforms over the current one in terms of efficiency.