This paper is aimed at the reduction of soot and NOx emissions, while maintaining reasonable temperature. For this goal, a computational model, Sprint CFD code, is incorporated with genetic algorithm (GA) to solve multi-objective optimization problem. Sprint CFD code analyzes the pollutants emissions, temperature and chemical species of the axisymmetric cylindrical furnace. An extended Genetic Algorithm called the \\\"Non-dominating Sorting Genetic Algorithm\\\" (NSGA-П) is used as an optimizer thanks to its ability to derive high accurate solutions. The target purpose functions are exit temperature, NOx, and soot emissions. The design variables are air inlet axial velocity, air inlet tangential velocity, diameter of droplets and air inlet preheating. The Pareto optimum solutions obtained from Sprint-NSGA-П are very useful to obtain optimal operational conditions. The solution shows the amount of NOx and soot emissions being kept under regulated values while the exit temperature is in the range of 1890 to 1990k.

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