This manuscript is concerned with the solution procedure of a fuzzy quadratic programming problem on risk adjusted return of portfolios by simulation-based genetic algorithm. In this manuscript, parameters of the programming problem are considered as triangular fuzzy numbers. The decision variables are assumed to be triangular fuzzy variables. In contrast to the classical technique, the proposed approach does not require the derivation of crisp equivalent form. The fuzziness of parameters and decision variables of the objective functions is first addressed using a ranking function formula implemented by MATLAB, which combines the strategy used in Genetic Algorithm. The property of fuzzy inequality constraint is used to handle the fuzziness of constraint functions. The feasibility requirements are maintained throughout the problem. A case study is used to demonstrate the proposed methodology.