In order to improve the forecasting precision of discrete grey model (DGM), a new modified DGM(1,1) model called discrete grey optimization model (DGOM) is created. The key of this model is a parallel transformation which is applied to the first order-accumulating generator operator (1-AGO) sequence. And the optimal parallel value c can be obtained based on the optimization method. The contrasted result of a numerical example shows that DGOM(1,1) yields a more accurate prediction capability than DGM(1,1) does. Integrating with other improved models such as optimized starting-point fixed discrete grey model and residual modified model, DGOM(1,1) can be characterized by even more accurate prediction for the grey modeling. Moreover, the mechanism of the parallel transformation to improve the forecasting precision and the relationship between DGOM(1,1) and another optimization of DGM are also discussed to demonstrate the characteristics of DGOM(1,1) more clearly. This work contributes significantly to improve grey forecasting theory and proposes more novel grey forecasting models.