Water shortages and the deterioration of water quality in the natural environment have a negative effect on social development of many countries. Therefore, optimizing the allocation of water resources has become an important research topic in water resources planning and management. An essential step in improving the utilization efficiency of water resources is the prediction of water supply and demand. Because it has a great number of merits, the grey prediction method has been widely used in population prediction and temperature prediction. However, it also has limitations such as low prediction precision since original data seriously fluctuates. This paper aims to handle the sample values by an innovative method utilizing moving-average technique (MA) model and optimizing the background values to make them more typical. Results proved that the prediction accuracy of the traditional model was effectively improved by the proposed method. The proposed model was then applied in the multi-objective planning to establish an optimal water resources allocation model for Beijing in the short-term (2020) planning timeframe, including local water resources, transfer water volumes, and other water supplies. The results indicated that industrial and agricultural water use could be well met, while domestic and environmental water resources may face a shortage.