This chapter explores optimization methods for wavelet design, focusing on techniques that enhance wavelet properties for specific applications. It begins with an overview of optimization methods, discussing their role in refining wavelet characteristics. The properties of practical optimization problems are examined highlighting constraints, objective functions, and solution spaces. The chapter then introduces evolutionary computation, a class of nature-inspired optimization techniques. Specific algorithms, including the genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO), are discussed in detail explaining their mechanisms and suitability for wavelet design. The chapter concludes with an application where a modified PSO algorithm is employed to design wavelets tailored to flute signal analysis demonstrating its effectiveness in optimizing wavelet properties for musical signal processing.