Chance-Constrained Multi-Choice Knapsack Problem （CCMCKP） is a class of NP-hard combinatorial optimization problems with important practical applications. However， there is a lack of research on the solution methods for this problem. The first framework for solving CCMCKP was proposed for this problem， and two solution methods were established based on this framework， including the dynamic programming-based method RA-DP and the genetic algorithm-based method RA-IGA. RA-DP is an exact method with optimality guarantee， but it can only solve small-scale problem instances within a time budget of 1 hour. In contrast， RA-IGA is an approximation method with better scalability. Simulation experimental results verify the performance of the proposed methods. On small-scale problem instances， both RA-DP and RA-IGA can find the optimal solutions. On the medium- and large-scale problem instances， RA-IGA exhibits significantly higher efficiency than RA-DP， always obtaining feasible solutions quickly within 1 hour. In future research on CCMCKP， RA-DP and RA-IGA can be considered as baseline methods， and the benchmark set considered in this work can also be used as a standard benchmark test set.