<p>Effective task scheduling is crucial in dynamic, resource-constrained environments such as mobile crowd computing (MCC), where inefficient workload distribution across heterogeneous smart mobile devices (SMDs) can degrade performance and discourage user participation. While genetic algorithm-based schedulers have been widely explored for distributed systems, many existing approaches treat load balancing as a secondary outcome rather than an explicit optimization objective, often leading to skewed resource utilization in MCC. This paper proposes MGA-LBaS, a structurally enhanced genetic algorithm-based load-balance-aware scheduling framework tailored for MCC. The proposed MGA-LBaS introduces a validity-preserving chromosome representation and a load-balance-aware novel mutation strategy that actively redistributes tasks across heavily and lightly loaded devices during evolution, ensuring feasible and balanced schedule throughout the optimization process. Load balancing is explicitly incorporated as a primary objective alongside makespan, resource utilization, speed-up, and energy consumption using a multi-criteria fitness formulation. Extensive experimental evaluation using real SMD profiles and multiple task–system configurations demonstrates that MGA-LBaS achieves more balanced and robust performance than representative heuristic and meta-heuristic schedulers. Statistical analysis and multi-criteria decision-making further confirm the superiority of MGA-LBaS in maintaining workload fairness without compromising overall efficiency, making it suitable for practical MCC deployments.</p>