In this paper, a comprehensive energy simulation environment is developed and presented to optimally select both building envelope features and heating and air conditioning system design and operation settings. The simulation environment is able to determine the building design features that minimize the life cycle costs. Three optimization algorithms are considered in the simulation environment including Genetic Algorithm, the Particle Swarm Algorithm and the Sequential Search algorithm. The robustness and the effectiveness of the three algorithms are compared to assess the performance of the simulation environment for various design applications and climatic conditions. In particular, the simulation environment has been applied to design single family homes in five US locations: Boulder, CO; Chicago, IL; Miami, FL; Phoenix, AZ; and San Francisco, CA. Optimal designs are determined to reduce life cycle costs with and without budget constraints. It is found that the optimal selection can reduce life cycle costs by 10-25% depending on the climate and type of homes. (C) 2011 Elsevier B.V. All rights reserved.