Hub location problems are widely studied in the area of location theory, where they involve locating the hub facilities and designing the hub networks. Hubs are special facilities that serve as switching, trahipment and sorting points in many-to-many distribution systems. Instead of serving each origin–destination pair directly, hub facilities concentrate flows in order to take advantage of economies of scale. Flows from the same origin with different destinations are consolidated on their route to the hub and are combined with flows that have different origins but the same destination. The consolidation is on the route from the origin to the hub and from the hub to the destination as well as between hubs. Here we consider capacity restrictions on incoming flow that must be sorted. Most of the proposed methods in hub network are based upon the condition that all time parameters are known exactly. This is a stringent assumption, which can cause difficulties in practice. In fact, there are many vaguely formulated relations and imprecisely quantified physical data values in real-world descriptions, since precise details are simply not known in advance. There could be an uncertainty in a number of factors, such as flow, capacity and costs. Thus, in these cases the solutions generated using deterministic models may not be very accurate. In this research flow between origin–destination pairs is considered fuzzy parameters. We formulate this problem with fuzzy chance-constrained programming and credibility measure. Chance-constrained programming (CCP) offers a powerful means of modeling stochastic decision systems with assumption that the stochastic constraints will hold at least ? of time, where ? is referred to as the confidence level provided as an appropriate safety margin by the decision-maker. By applying this formulation, there are two uncertain function (objective function and hub capacity restriction). A fuzzy simulation-based genetic algorithm is provided for solving the proposed model problems. Fuzzy simulation is used for estimation capacity restriction and objective function and genetic algorithm is applied for search of optimum solution. To illustrate the modeling idea and the effectiveness of the proposed algorithm the fuzziness Australian Post (AP) data was applied. The result show that, with increasing in confidence level of capacity restriction, the best solution point may be changed and with increasing confidence level of objective function, best objective will be changed.