<p>Influence maximization in social networks has received increasing attention, particularly in applications where fairness among demographic groups is an important concern. However, many existing approaches either overlook group-level disparities or primarily optimize influence spread without explicitly modeling fairness-related trade-offs. In this paper, we propose a group-aware multi-objective evolutionary framework that decomposes seed sets into group-specific sub-solutions. Each demographic group maintains independent subpopulations, which are evolved to improve local influence within the corresponding group. The optimized sub-solutions are then recombined into full solutions, which are globally evaluated using two objectives: total influence spread and inter-group fairness, estimated via pre-generated Reverse Reachable sets. This design reduces the need for repeated diffusion simulations during optimization and can improve computational efficiency. To further enhance diversity and reduce the risk of premature convergence, we introduce a sampling-based strategy that generates new composite solutions by probabilistically selecting best, worst, or random sub-solutions from each group. Multi-objective selection is performed using Pareto dominance, front ranking, an elite archive, and crowding distance. The proposed framework maintains population diversity through subpopulation co-evolution and adaptive sampling. Experimental evaluations conducted on eleven synthetic networks and five real-world datasets show that the proposed method achieves competitive or improved performance relative to strong baselines, considering both fairness of coverage across groups and overall influence propagation.</p>