A genetic algorithms-based multiobjective optimization study has been carried out for the bottom gas injection process practiced in the steelmaking vessels. Two objective functions, one dealing with the efficacy of mixing and the second one characterizing the stress level at the ladle walls, have been evaluated from a numerical solution of coupled Reynolds averaged transient Navier-Stokes equations, energy and species conservation equations, as well as governing equations for turbulent kinetic energy and its dissipation rate. A Pareto front has been constructed by using the recently proposed Strength-Pareto Evolutionary Algorithm and the corresponding flow fields have been computed by using a pressure-based finite volume methodology. It has been inferred that judicious combinations of the two objective functions can be obtained from the simulation results, leading to an optimal process control.