In addition to inequality constraints, many mathematical models require equality constraints to represent the practical problems appropriately. The existence of equality constraints reduces the size of the feasible space significantly, which makes it difficult to locate feasible and optimal solutions. This paper presents a new equality constraint handling technique which enhances the performance of an agent-based evolutionary algorithm in solving constrained optimization problems with equality constraints. The technique is basically used as an agent learning process in the agent-based evolutionary algorithm. The performance of the proposed algorithm is tested on a set of well-known benchmark problems including seven new problems. The experimental results confirm the improved performance of the proposed technique.