<![CDATA[Employee reward distribution plays an important role in increasing motivation and retention. Conventional employee reward models often contain elements of subjectivity and do not reflect the overall contribution of employees. This can lead to unfairness and reduce work motivation. Traditional models in reward allocation often fail to incorporate a comprehensive evaluation of employee performance based on various criteria. This study develops a multi-criteria performance-based reward allocation model using Genetic Algorithm (GA) as an optimization approach. The model is designed to consider various performance indicators such as performance, attendance, tenure, and innovation in the process of fair and proportional bonus distribution. The optimization results show a very strong positive correlation (r = 0.99) between the employee's composite score and the amount of bonus allocated. In addition, the simulation of the evolution of fitness values shows a constant increase in both the average and the best values of the solution population, confirming the effectiveness of the genetic algorithm exploration and convergence process. This model produces a bonus distribution that is proportional to employee contributions, reflecting the principles of fairness, meritocracy, and transparency in the reward system. In addition, this model is flexible to budget changes and can be replicated for real implementation. The scientific contribution of this research lies in the application of a heuristic approach to multi-criteria optimization in the context of human resource management, complementing the literature that has so far been dominated by linear models.]]>
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