The organizational algorithm is examined as a computational approach to representing interpersonal learning. The structure of the algorithm is introduced and described in context to the simple genetic algorithm. A comparison is made of the performance of both algorithms with respect to three different test functions: a simple single-peaked function, the standard Matlab "peaks" function (Mathworks 1998), and a multiple occurrence of a single-optimum Gaussian function. Algorithm performance is discussed relative to traditional optimization figures of merit as well as with respect to a learning analogy.