To address the optimization of multi-objective mathematical models, utility functions are often utilized for each objective. While current research in utility-based multi-objective optimization typically assigns a single utility function to each objective, this approach may not align with real-world complexities. In this context, an unconstrained multi-objective model is introduced, where multiple utility functions are linked to each objective. To account for the inherent uncertainties in these utility functions, a fuzzy probabilistic framework is integrated. Given that the utility functions are expressed in fuzzy terms, the overall utility function becomes a fuzzy nonlinear model. Since traditional methods are inadequate for solving such models, a defuzzification process is applied to transform the fuzzy utility function into a crisp nonlinear form. Specifically, the maximum technique is used to defuzzify the conditional utility functions. Subsequently, an established optimization method is employed to solve the resulting single-objective nonlinear model. The findings demonstrate that the approach remains effective in generating solutions even as utility functions adapt to environmental changes. The validity of this method is confirmed through the successful resolution of a test problem.