The properties of nanoparticles synthesized from green synthesis are determined by the combined effect of many factors involved in the process of green synthesis. Despite the significant growth in the plant-extract-mediated synthesis of nanoparticles, the issue of optimization of process parameters in biosynthesis still needs to be addressed. The optimization of various controlling factors which are profound determinants of green synthesis is an important aspect in standardizing the most effective and quality nanomaterial. Response Surface Methodology (RSM) is a robust statistical method to design an experiment to evaluate the combined effect of several factors for obtaining the optimum conditions that yield desirable output. The RSM is a systematic fusion of the design of the experiment, statistical modeling, and optimization method. This chapter discusses the practical considerations in designing and analyzing RSM methodology to optimize the response. The stepwise procedure of RSM which includes a selection of controlling factors, determination of factor levels, coding the factor, running the experiment, fitting second-order response model, assessment of the significance of factor effects, validation of the fitted model, and finally optimization concept has been discussed. Further, the concept of RSM has been illustrated using an arbitrarily chosen dataset to optimize the temperature, reaction time, and pH that maximize the size of Silver Nanoparticles (AgNPs).