Cross aging face recognition ability will decrease to recognize someone's face after a certain time. Adding synthetics face images at a certain age generated from face aging architecture is one way to increase the performance of cross aging face recognition. A synthetics face image can create use the Generative Adversarial Network-based architecture. The current Generative Adversarial Network-based in face aging still needs high computation to create a model. Based on that reason we proposed a new optimal variant of Identity Preserving Conditional Generative Adversarial Network (IPCGAN), to generate a synthetic face image at certain age groups. In the proposed architecture, change made at the structure in the generator module, age classification module, and change the objective function to increasing the accuracy performance when generates a realistic synthetic face image in certain age groups and also speed up the training time. Modification in the age classifier at the proposed network forces our architecture to generate better synthetics face in certain age groups. Evaluation using Facenet, and Age prediction shows our method accuracy has 4.2% better results in k-NN classification, 3.6% better accuracy results in SVM classification, 8.6% better accuracy result in age verification, and 4.5% fewer accuracy results in age prediction.