INTRODUCTION In order to reduce building energy consumption most effectively@ heating and cooling loads due to the building envelope must be addressed early in the design process. Several design parameters can have an effect on these loads@ including the shape of the building@ wall and roof construction@ foundation type@ insulation levels@ window type and area@ thermal mass@ and shading. All of these parameters interact and affect the energy performance of the building. Traditionally@ this type of analysis has been done with parametric runs using a building simulation engine such as DOE-2 (Winkelmann@ 1993) or EnergyPlus (Crawley@ 2000). However@ varying one parameter while leaving others building envelope features constant can potentially miss important interactive effects@ and full combinatory parametric studies are usually infeasible. A better solution is to couple an optimization algorithm to a simulation engine in order to find a minimum for a given cost function including life-cycle cost@ annual operating costs@ and annual energy use (Wright@ 2002; Caldas and Norford@ 2003; and Ouarghi and Krarti@ 2006). The objective of this paper is to compare three different optimization techniques to assess their robustness and efficiency for application in building envelope optimization. Robustness is a measure of the algorithm's ability to minimize the cost function@ while efficiency is a measure of its speed which is defined in this study as the number of simulations required to reach the minimum cost level. The three methods investigated in this paper include the sequential search used in the Building Energy Optimization or BEopt tool (Andersen@ et al. 2004)@ genetic algorithms or GAs (Goldberg@ 1989 and Haupt and Haupt@ 2004)@ and particle swarm optimization or PSO (Wetter@ 2006). Each of these methods does not require the calculation of differentials for the cost function@ but instead uses discrete values of the cost function to determine the parameter values of the next iteration (i.e. direct search).