Predicting the main cutting force during turning is of great importance as it helps in setting the appropriate cutting parameters before machining starts. Again, optimization of cutting parameters is one of the most important elements in any process planning of metal parts as economy of machining operation plays a key role in gaining competitive advantage. This paper presents an experimental study of main cutting force in turning of AISI 1040 steel and developing a model of the main cutting force during turning using Response surface Methodology (RSM) as well as optimization of machining parameters using Genetic Algorithm (GA). The second order empirical model of the main cutting force in terms of machining parameters have been developed based on experimental results. The experimentation has been carried out considering three machining parameters: cutting speed, feed rate and depth of cut as independent variables and the main cutting force as the response variable. The formulated model has been validated against new set of experimental values using Mean Absolute Percent Error (MAPE) method. The Genetic Algorithm approach is also used to optimize the cutting parameters to keep the main cutting force to a minimum.