This chapter addresses an algorithm, namely Spider Monkey Optimization (SMO) for the researchers interested in optimization, swarm intelligence is an engaging realm. Several nature-inspired computing techniques and algorithms have been evolved by simulating the swarming traits of creatures such as ants, fishes, honey-bees, fireflies, etc. The results put forth are indeed extremely promising when applied to find the best-fit solution to a real-world optimization problem. Spider Monkey Optimization Algorithm (SMO) is detailed which is modeled on the basis of the social behavior of spider monkeys. This chapter focuses on detailing the nuances of SMO specifically the phases involved, namely the Leader Phase, Learning Phase, and Decision Phase. It also aims to give an introduction to the basic mathematical jargon and fundamentals that are required to model an SMO algorithm for finding the best-fit solution to an in-hand problem. Various variants of SMO are also covered in this chapter with a detailed overview of the pros and cons of each of the variants focusing on the research gaps. A wealth of information in the form of practical use-cases and implementation nuances is provided in this section, which will be a ready reckoner for a researcher reader aiming to flatten the learning curve associated with experimenting on basic SMO as well as trying out its variants for optimization problems in their specific realm of research. Further, the chapter gives an overview of a transformed version of SMO, Binary SMO with a real-world application and its comparison against other optimization techniques. It also comprises a case study of SMO where it is applied to Traveling Salesman Problem (TSP). SMO is used for this optimization problem to find the least travel cost covering all the cities. The chapter concludes by giving the reader an insight into the latest research trends in SMO.