This paper addresses the problem of portfolio selection with fuzzy parameters from a perspective of chance-constrained multiobjective programming. The key financial criteria used here are conventional, namely, return, risk, and liquidity; however, we use short- and long-term variants of return rather than a single measure of an investor's expectations in respect thereof. The proposed model aims to achieve the maximal return (short term as well as long term) and liquidity of the portfolio. It does so at a credibility, which is no less than the confidence levels defined by the investor. Further, to capture uncertain behavior of the financial markets more realistically, fuzzy parameters used here are such as those characterized by general functional forms. To solve the problem, we rely on a specially developed algorithm that hybridizes fuzzy simulation and real-coded genetic algorithm. Numerical experiments are included to showcase the applicability and efficiency of the model in a real investment environment.