Predicting climate variability is challenging. The Pacific Ocean’s El Niño-Southern Oscillation (ENSO) affects global climate variability and its complexity. Understanding and mitigating global climate variability requires ENSO episode modeling. Evolutionary Algorithms (EA) are used here to create a library of simple equations to characterize El Niño and La Niña events. The results show that most El Niño events can accurately be modeled with acceptable stability, with the coefficient of determination (R2 ) between 0.72 and 0.99 for weak events, 0.84 and 0.98 for moderate events, 0.86 and 0.99 for strong events, and 0.91 and 0.98 for very strong events. For the La Niña events, R2 was found to be in the range 0.75–0.98 for weak events, 0.74–0.99 for moderate events, and 0.86–0.98 for strong events. The ANOVA test’s F-value showed a good model fit with p-values below 0.05 for all events. The study developed a library-database of equations to better understand ENSO events, a methodology that can be applied to other time series or fields.