The test case generation and prioritization of industrial Cyber-Physical Systems (CPSs) face critical challenges and simulation-based testing is one of the most commonly used techniques for testing these complex systems. However, simulation models of industrial CPSs are usually very complex and executing the simulations becomes computationally expensive, which often make it infeasible to execute all the test cases. To address these challenges, this paper proposes a multi-objective test generation and prioritization approach for testing industrial CPSs by defining a fitness function with four objectives and designing different crossover and mutation operators. We empirically evaluated our fitness function and designed operators along with five multi-objective search algorithms (e.g., Non-dominated Sorting Genetic Algorithm (NSGA-II)) using four case studies. The evaluation results demonstrated that NSGA-II achieved significantly better performance than the other algorithms and managed to improve Random Search for on average 43.80% for each objective and 49.25% for the quality indicator Hypervolume (HV).