Many optimisation problems in finance are difficult to solve because of multiple local optima or objective functions that are not well-behaved in other ways. The thesis comprises several essays on the application of optimisation heuristics to such problems. More specifically, we use methods like Differential Evolution, Particle Swarm Optimisation and Threshold Accepting to solve a selection of financial models. Examples that are discussed are portfolio construction, the calibration of option pricing models (eg, the Heston model) and yield curve models (the Nelson-Siegel-Svensson model), and robust/resistant regression (eg, Least Quantile of Squares and Least Trimmed Squares). In sum, we present evidence that heuristics perform extremely well on these problems and are thus ideal techniques for practical financial optimisation.