In this work, the Stochastic Spiral Optimization (SSO) algorithm, which is an improvement of the deterministic spiral optimization (DSO), is introduced. SSO performance was evaluated with a group of five population-based optimization algorithms including the original DSO algorithm. Performance tests showed that the modifications on spiral optimization scheme allowed improving the exploration and exploitation skills with a wide range of control parameters, which produced lower optimization errors to a variety of standard functions. SSO admitted of selecting several combinations of its control parameters, which kept the optimization errors into an acceptable order. The SSO efficiency and effectiveness were noticeably increased, which was proven by the experimental results.