A general framework of fuzzy random chance maximization multiobjective programming is considered in this paper. The crisp counterpart model is presented for the fuzzy random chance maximization multiobjective linear programming with L-R type fuzzy random coefficients. Fuzzy random simulation is adopted to handle general fuzzy random objective functions and fuzzy random chance constraints which are usually hard to be converted into their crisp equivalents. Combining with fuzzy random simulation and the compromise approach-based genetic algorithm, we design a hybrid intelligent algorithm to solve the general fuzzy random chance maximization multiobjective programming problem. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed algorithms.