Acoustic cell clarification is an upcoming novel primary harvest technology for biopharmaceutical manufacturing. It utilizes a 3D standing acoustic wave to separate cells in the form of clusters from the cell culture fluid in a continuous fashion. In this study, multi-objective optimization was performed on the acoustic clarification of CHO cells using a Cadence^TM acoustic separator (CAS). Three objectives, namely cell separation efficiency (CSE), protein recovery (PR), and residence time (RT) of harvest, were evaluated using a hybrid model approach. Next, the non-dominated sorting genetic algorithm (NSGA-II) was successfully employed for tri-objective optimization using critical process parameters, namely feed flowrate (FF), concentrate flowrate (CF), and load cell density (LCD) as the decision variables. The optimization study was performed for three different configurations of the acoustic chambers: series, series–parallel, and parallel to obtain the optimal design and set of solutions. The results of the NSGA-II algorithm revealed the relationships between CSE, PR, and RT and evaluated a tradeoff and an optimal balance between them was achieved. The series–parallel configuration was evaluated as the most optimal design, giving CSE: 80.20 %, PR: 82.61 % and RT: 10.42 mins at an LCD of 30 × 10⁶ cells mL⁻¹. An application study was conducted to obtain the optimal solution set at different LCDs in a single run. Further, controller studies were also performed to apply corrective actions in case of feed turbidity disturbances in order to maintain a steady state. Overall, this is the first attempt to develop a multi-objective optimization study for CHO cell clarification, and this may be utilized as a tool for simulation, design, optimization, and control of any cell harvest unit operation.