The efficiency of metaheuristics depends on parameters. Often this relation is defined by statistical simulation and have many local minima. Therefore, methods of stochastic global optimization are needed to optimize the parameters. In this paper a short presentation of the basic ideas of the Bayesian Heuristic Approach is given. The simplest knapsack problem is for initial explanation of BHA. The possibilities of application are illustrated by a school scheduling problem and other examples. Designed for distance graduate studies of the theory of games and markets in the Internet environment. All the algorithms are implemented as platform independent Java applets or servlets therefore readers can easily verify and apply the results for studies and for real life heuristic optimization problems. To address this idea, the paper is arranged in a way convenient for the direct reader participation. Therefore, a part of the paper is written as some 'user guide.' The rest is a short description of optimization algorithms and models.