Moo library provides building blocks to build custom genetic algorithms in Haskell. They can be used to find solutions to optimization and search problems. Variants supported out of the box: binary (using bit-strings) and continuous (real-coded). Potentially supported variants: permutation, tree, hybrid encodings (require customizations). Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II.