The aim of this research is to present an optimization model for fintech based on artificial intelligence indices in the financial market. This research is exploratory in nature due to the model presentation and is considered applied since its results are utilized by stakeholders. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) was employed as a metaheuristic method to solve nine problem simulations. The results obtained from this method were then compared with the epsilon-constraint method, and the relationship between the solutions indicated that the developed NSGA-II algorithm is capable of reaching appropriate solutions in a shorter time compared to the epsilon-constraint method, particularly for large-scale problem tests. The outcomes from solving the proposed mathematical model through the presented nine problem simulations were solved using the specified algorithms in GAMS and MATLAB software. The model considered in this research is a bi-objective model aimed at minimizing inter-cell movements and fintech purchases (cell formation) and maximizing the relationships of AI operators with network considerations and operator efficiency on fintechs (operator allocation). This model not only improves the efficiency of fintechs but also provides a novel and effective approach to adapting to various challenges in the financial market. Therefore, utilizing this optimization model can enhance performance and profitability in the financial market and contribute to development and progress in the financial space.