Many optimization problems in theoretical and applied science are difficult to solve: they exhibit multiple local optima or are not ’well-behaved’ in other ways (e.g., have discontinuities in the objective function). The still-prevalent approach to handling such difficulties -- other than ignoring them -- is to adjust or reformulate the problem until it can be solved with standard numerical methods. Unfortunately, this often involves simplifications of the original problem; thus we obtain solutions to a model that may or may not reflect our initial problem. But there is yet another approach: the application of optimization heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable of handling non-convex optimization problems with all kinds of constraints, and should thus be ideal candidates for many optimization problems. In this talk we motivate the use of such methods by first presenting some examples from finance for which optimization is required, and where standard methods often fail. We briefly review some heuristics, and look into their application to finance problems. We will also discuss the stochastics of the solutions obtained from heuristics, in particular we compare the randomness generated by the optimization methods with the randomness inherent to the problem.