In this study, we investigate the chance-constrained portfolio optimization problem, which aims to determine a minimum-cost allocation of a unit investment among multiple assets with uncertain returns. Specifically, the objective is to allocate investments such that the overall return meets or exceeds a predefined level with high probability, ensuring controlled risk exposure. To address this issue, we employ the Sample Average Approximation (SAA) method to approximate the uncertain return distribution using an empirical distribution derived from a finite sample set. This leads to an approximated problem involving a DC (Difference of Convex functions) constrained function, which is then solved by a novel approach based on DC programming and DCA (DC Algorithm). Extensive numerical experiments are conducted to assess the performance of the proposed approach under varying parameter settings. The results demonstrate the effectiveness and robustness of our method in achieving cost-efficient portfolios with controlled risk.