This paper provides a theoretical framework of dependent-chance progra
mming, as well as dependent-chance multiobjective programming and depe
ndent-chance goal programming which are new types of stochastic optimi
zation. A stochastic simulation based genetic algorithm is also design
ed for solving dependent-chance programming models.