In the injection molding of plastic optical lenses, the molding conditions have critical effects on the quality of the molded lenses. Since there are many molding parameters involved in injection molding process, determination of the molding conditions for lens molding is very important in order to precisely control the surface contours of an optical lens. Therefore this paper presents the application of neural network in suggesting the optimized molding conditions for improving the quality of molded parts based on data of FE Analysis carried out through CAE software, Timon-3D. Suggested model in this paper, which serves to learn from the data of FE Analysis and induce the values for optimized molding conditions. has been implemented for searching the molding conditions without void and with minimized thickness shrinkage at lens center of injection molding optical lens. As the result of this study. we have confirmed that void creation at the inside of lens is primarily determined by mold temperature and thickness shrinkage at center of lens is primarily determined by the parameters such as holding pressure and mold temperature.