Using the fuzzy AR model, of which the coefficient is symmetry fuzzy triangular number to forecast the investment returns, and the covariance of the middle value of the fuzzy return to measure the investment risk, we expand the singleperiod meanvariance model of Markowitz into multiperiod fuzzy investment programming on condition that the liquidity is introduced as the constraint. Translating the forecasting return into the clear investment return by the defuzzifier formula at the end of each phase, we establish a twophase chance programming in order to look for the Pareto solution of the binaryobjective programming, and resolve this programming with the particle swarm algorithms which includes the factor of genetic cross and mutation. To choose 32 stocks of the SSE 50 to make the empirical analysis, and use of the discrete multiperiod meanvariance model to prove the validity of the establishing model, the results show that: the investment return obtained according to the optimal portfolio of the twophase chance programming is in the permission interval of the fuzzy return value, and its forecasting error is less; the risk of the twophase chance programming is much lower than the risk of the multiperiod MV model on condition than the constraint is the same investment return.