This paper explores the application of the Chopped Random Basis (CRAB) algorithm, integrated within the Quantum Toolbox in Python (QuTiP), to control and manipulate entanglement dynamics in a two-qubit quantum system. The primary focus is on enabling the transition from a perturbed separable state to a maximally entangled state via the strategic design of control pulses. By employing a tailored Hamiltonian, we optimize the pulses to meet a predefined fidelity error target, thus assessing the algorithm’s efficacy in achieving precise quantum state transformations. Our results illustrate the robust capabilities of CRAB for advanced quantum state control, underscoring its profound implications for quantum computing and information processing. This study not only demonstrates the practical utility of CRAB in enhancing the control over quantum entanglement but also contributes to the broader field of quantum dynamics optimization.