In metal cutting operations, the proper selection of machining parameters and cutting fluids affects the cutting mechanism, which is associated to the cutting forces, tool wear, cutting temperature and surface finish of the machined part. For this purpose, the effect of cutting fluid types is investigated as a function of three machining parameters (cutting speed, feed rate and approach angle) on responses (cutting force, cutting temperature, tool wear and surface roughness) while turning titanium alloy under Nano-Fluid based Minimum Quantity Lubrication (NFMQL) environment. The experiments are conducted based on response surface methodology and a combined objective function using the output parameters was generated. Further, the parametric optimization was performed through two evolutionary techniques, i.e., Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The results are analyzed and also compared with the traditional desirability function approach technique.