Achieving higher speed to attain higher productivity in continuous casting process is not an easy task as perturbations in casting speed may lead to inter-roll bulging causing the meniscus level to fluctuate leading to increase in chances of breakout. However, increasing the spray cooling in a random manner as a measure of reducing the bulging and thereby controlling meniscus level fluctuation is not the way to achieve higher production rate because this would lower the exit temperature of the slab from the rollers. In that case, the temperature of the slab entering the reheating furnace, which is placed just ahead of the roll caster, would have to be increased leading to higher fuel consumption. The objective of any thin slab casting practitioner who wants to reduce meniscus level fluctuation by eliminating the sources of the fluctuation, therefore, is to find the optimal spray distribution that minimizes the bulging, maximizes the slab exit temperature, and maximizes casting speed simultaneously. This three-objective (mutually conflicting) optimization problem is solved here adapting the elitist version of the nondominated sorting genetic algorithm (NSGA II). An industrial continuous casting case study has been formulated under the above mentioned optimization framework, solved and analyzed in full details to unveil embedded salient operating principles for the casting process under consideration. In this way, the nominal casting speed and productivity could be increased by more than 30% and 10%, respectively, with a return on investment of one month along with few other intangible benefits. This problem formulation methodology is very generic in nature and can be applied to many complex problems from various fields.