The total number of infections (epidemic size), and the time needed for the infection to die out (epidemic duration), represent two of the main indicators for the measuring gravity of infectious disease epidemics in humans. A few attempts have been made to address the problem of controlling both the epidemic size and duration simultaneously from a theoretical point of view, by primarily using the Optimal control theory. In this study, a multi-objective optimal control problem has been simulated to gauge the success of the lockdowns in India. To accomplish this objective, a system dynamics modeling was used to simulate the Susceptible-Infected-Quarantined-Removed epidemic model. A set of sensitivity experiments for different scenarios allows illustrating the model's behavior and its value for decision-makers regarding the lockdown intensity. The simulation of the model presents various scenarios, wherein the cost-benefit analysis of lockdown was done. Notably, the lockdown success intensity was defined, post which, the findings indicate that the states of Uttar Pradesh, Kerala, Panjab, Jammu, Kashmir, Haryana, and Bihar have achieved more than 90% lockdown success intensity. Further, it was observed that these states could effectively implement lockdowns by strictly enforcing social distancing measures during the early stages of the virus spread, which in turn resulted in the high success rate of lockdowns.