In recent years, research has focused on designing buildings with higher energy efficiency and lower emissions by considering multiple objectives. This can impact financial savings, smaller environmental footprints, and energy consumption optimization. The purpose of the current study is to develop a new technique to solve this challenging multiple-objective optimization problem. While there are different methods to solve optimization problems, based on the NLP theory, there is not any metaheuristic algorithm that can solve all the problems accurately. Sometimes, the outcome of a basic algorithm is a local optimum. Therefore, to reach the global optimum, we propose the Improved Billiard-based Optimization Algorithm (IBOA). Moreover, in some cases, the basic model suffers from premature convergence, which prevents reaching an accurate result. Hence, this study aims to solve this problem and attain better convergence results using the proposed method to minimize CO₂-eq emissions and life cycle costs. The design variables include some parameters of the envelope of a single-family residential dwelling to indicate the efficiency of the presented method. Based on the Pareto optimum solutions achieved, it is proved that the method is effective.