<p>The recently proposed Battle Royale Optimization (BRO) algorithm provided an acceptable trade-off between exploration and exploitation. In our previous work, we proposed single-objective binary, multimodal, and unconstrained multi-objective versions of this algorithm. However, most of the real-world problems are constrained multi-objective in nature. In multi-objective optimization, it is necessary to simultaneously optimize a number of objectives, which are typically in conflict with each other, over a feasible set that is determined by constraint functions. This paper introduces the Constrained Multi-Objective BRO (C-MOBRO) algorithm, a novel computational approach designed to address complex optimization problems characterized by multiple conflicting objectives and constraints. The performance of the C-MOBRO is evaluated on CEC2021 benchmark problems, which includes 50 benchmark suits consisting of a wide range of real-world constrained multi-objective engineering and optimization challenges. This benchmark suite has also been experimented with several state-of-the-art constrained multi-objective algorithms. This study evaluates the C-MOBRO using the same performance metrics as the CEC2021 benchmark: Hyper-Volume (HV), Feasibility Rate (FR), and Constraint Violation (CV), separately calculated as best, worst and mean values. The obtained results show that the C-MOBRO is competitive edge with state of the art constrained multi-objective optimization algorithms.</p>