<p>Solving combined economic emission dispatch (CEED) problem optimizes power generation by balancing cost minimization with emission reduction, addressing economic and environmental goals simultaneously. This trade-off results in a Pareto front, where each non-dominated solution represents an optimal balance between costs and emissions. Decision-makers can select solutions based on their preferences. This paper proposes an Epsilon-based multi-objective genetic algorithm (MOGA) to solve the CEED problem. By integrating evolutionary techniques and Epsilon constraint methods, the proposed method actively explores diverse solutions, avoiding local optima and enhancing the Pareto front. The two-objective CEED problem is reformulated into two single-objective problems, alternately minimizing cost or emissions. The Epsilon constraint algorithm accelerates the search for optimal solutions. Two quality indicators are proposed to evaluate the Pareto fronts. The first indicator measures solution spread; it assesses the diversity of generator settings and the dominated volume. The second indicator evaluates the uniformity of solution distribution, where smaller distances indicate better uniformity. High spread and uniform distribution signify superior Pareto fronts. If diversity is insufficient, the proposed Epsilon-based MOGA continues to refine the front. The performance of the proposed method was tested on IEEE 30-bus and 118-bus systems, showing improved results compared to RNSGA-II, the Epsilon constraint algorithm, and NSGA-II. The proposed method produced more diverse and uniformly distributed non-dominated solutions; it offers grid operators a broader range of options to balance costs and emissions. Additionally, it achieved lower costs and emissions. The computational time for the larger IEEE 118 system is manageable and does not increase exponentially compared to the smaller IEEE 30 system. </p>