The challenge of expensive many-objective optimization problems (EMaOPs) is how to select excellent solutions as the number of objectives increases. Current research tends to focus on either convergence or diversity, both of which are ineffective for solving EMaOPs with complex Pareto fronts. For this reason, we propose a novel Dynamic Balance Sorting and Co-evolutionary Algorithm (DBS-CEO) for EMaOPs. DBS-CEO employs a dynamic balance sorting algorithm that combines dynamic decomposition sorting with dimensionality-decreasing non-dominated sorting. DBS balances convergence and diversity dynamically during the sorting process and utilizes boundary information to sort population solutions. DBS-CEO uses a co-evolutionary algorithm with Generative Adversarial Networks as an auxiliary optimizer for MOEA/D to generate better solutions. In comparison with five state-of-the-art SAEAs, DBS-CEO achieves competitive performance and computational efficiency on both commonly used benchmark problems.