To improve the calibration accuracy and calibration efficiency of cognitive diagnostic computerized adaptive testing (CD-CAT) for new items and, ultimately, contribute to the widespread application of CD-CAT in practice, the current article proposed a Gini-based online calibration method that can simultaneously calibrate the Q-matrix and item parameters of new items. Three simulation studies with simulated and real item banks were conducted to investigate the performance of the proposed method and compare it with the joint estimation algorithm (JEA) and the single-item estimation (SIE) methods. The results indicated that the proposed Gini-based online calibration method yielded higher calibration efficiency than those of the SIE method and outperformed the JEA method on item calibration tasks in terms of both accuracy and efficiency under most experimental conditions.