A substitution box is a vector Boolean function that is used in various cryptographic systems. The strength or weakness of a substitution box, in terms of different metrics, enables us to assess the resistance capacity of a cryptographic system against different types of attacks, such as linear or differential cryptanalysis. In this work, we present a substitution box generation scheme that implements the Chimp Optimization Algorithm supported by Opposition based Learning. We analyze this scheme in terms of the metaheuristics' behavior regarding the interaction between solutions and the substitution boxes generated based on non-linearity. The results indicate that the proposed scheme outperforms the original version and is suitable for generating substitution boxes that can be used in specific applications of cryptographic systems.