A sequential well placement strategy is important for field development planning under geological uncertainty, because reservoir conditions can change between drilling stages. Motivated by this challenge, this study proposes a hybrid framework that combines convolutional neural networks (CNNs) with a genetic algorithm (GA). The goal is to determine optimal well locations efficiently, while reducing reliance on full-physics reservoir simulation. The methodology uses OPM Flow to generate training datasets for two consecutive six-month periods. This allows the CNN proxy to learn the relationship between permeability realizations, well coordinates, and cumulative oil production. The trained proxies then guide the GA-based optimization in each period. Results for the Egg model show strong predictive performance in both stages. The coefficients of determination are 0.76 and 0.82 for training data, and 0.64 and 0.63 for testing data, in the first and second periods, respectively. In addition, the proxy-based optimization required only about 26% of the computational time of direct simulation in the first period, and roughly 15% in the second. Production estimates were maintained within a 5% error margin. Overall, the proposed sequential, proxy-assisted approach is accurate and computationally efficient for well placement optimization under geological uncertainty.