Products with complicated shapes require superior surface finish to perform the intended function. Despite significant developments in technology, finishing operations are still performed semi automatically/manually, relying on the skills of the machinist. The pressure to produce products at the best quality in the shortest lead time has made it highly inconvenient to depend on traditional methods. Thus, there is a rising need for automation which has become a resource to remain competitive in the manufacturing industry. Diminishing return of trading quality over time in finishing operations signifies the importance of having a pre-determined trajectory (tool path) that produces an optimum surface in the least possible machining time. Tool path optimization for finishing process considering tool kinematics is of relatively low importance in the present scenario. The available automation in grinding processes encompass around the dynamics of machining. In this paper we provide an overview of optimizing the tool path using evolutionary algorithms, considering the significance of process dynamics and kinematics. Process efficiency of the generated tool movements are studied based on the evaluation of relative importance of the finishing parameters. Surface quality is analysed using MATLAB and optimization is performed on account of peak to valley height. Surface removal characteristics are analysed based on process variables that have the most likely impact on surface finish. The research results indicated that tool path is the most significant parameter determining the surface quality of a finishing operation. The inter-dependency of parameters were also studied using Taguchi design of experiments. Possible combinations of various tool paths and tool influencing parameters are presented to realize a surface that exhibits lowest errors. / European Horizon 2020 Project SYMPLEXITY