<p>Optimization algorithms are crucial for solving some of the most intricate problems in engineering and science. Among these methods, meta-heuristic methods use stochastic elements to reach convergence, which is helpful in problems where the landscape is too complex for deterministic exploration. This paper introduces Cell Division Optimizer (CDO), a new meta-heuristic algorithm inspired by the two cell division processes: mitosis and meiosis. Mitosis is used to model exploitation whereas Meiosis is used for exploration. The method incorporates genetic diversity using mutation and crossovers, enhancing the algorithm’s ability to avoid premature convergence at local optima. The proposed method is evaluated over 50 well-known benchmark functions which include simple, unimodal, multimodal, and complex landscape benchmarks. Experiments are also performed for two classical engineering problems to verify the applications of CDO in real-world problems. This performance is compared with eight state-of-the-art algorithms including recently proposed methods and their specialized variants. A statistical analysis was performed to verify the significance of the results. Statistical analysis of 390 sub-experiments shows that CDO significantly outperformed competitors in 280 cases. In our experiments, it is seen that CDO consistently results in giving better solutions which is verified by studying the standard deviation among the results of the experiments. Finally, a convergence analysis is conducted for all the chosen methods. In most experiments, CDO converges to the global best earlier than the other algorithms, while avoiding local optima values.</p>