This paper presents a two-phase quantitative approach for enhanced index investing based on the mean-variance model and the goal programming method. In the first stage, we use the mean-variance theory to select better performing stocks for an investment pool. Then, in the second stage, we use a goal programming method to weight the selected stocks by balancing both the tracking error and the rate of return. In addition to the theoretical formulation, we construct a spreadsheet-based decision support system (DSS) based on the transaction data to help resolve the index tracking problem. The paper contributes to the literature in two ways. For academics, we present original discussions on combining an interdisciplinary mean-variance model and a goal programming method. Unlike the conventional approach used for enhanced index investing that requires a fund manager to actively buy and sell stocks to improve returns, our approach is based on historical data and deduces subjective judgments. Meanwhile, for practitioners, we present an original discussion on using a DSS to support index investing. The results of an empirical survey of the Taiwan stock market are also presented.