This dissertation deals with the development of discrete-continuous optimization models and algorithms that address sustainable design and synthesis of energy systems. Specifically, contributions to the following two energy systems are presented, namely an algal biofuel and bioproduct manufacturing system and a shale gas processing and chemical manufacturing system. The algal biofuel and bioproduct manufacturing system is a promising renewable energy system. In the first related project, we propose a comprehensive superstructure of algal biofuel and bioproducts manufacturing processes and a corresponding mixed-integer fractional programming model to determine the optimal process design with the optimal functional unit based economic and environmental performance. Moreover, we develop a tailored global optimization algorithm to efficiently solve the resulting problem. In the second related project, we propose a two-stage adaptive robust mixed-integer fractional programming model to maximize the return on investment under uncertainty in market related parameters. A tailored optimization algorithm is developed to solve the multi-level optimization problem that cannot be handled directly by any off-the-shelf optimization solvers. In the third related project, we develop a consequential life cycle optimization framework that simultaneously optimizes consequential environmental impacts and economic performance. The shale gas processing and chemical manufacturing system is a conventional energy system, but has gained momentum in recent decades due to the successful application of advanced extraction technologies. In the first related project, we develop a general framework for combining product distribution optimization of chemical reactions and superstructure optimization of process flowsheets. A comprehensive superstructure of shale gas processing and chemical manufacturing processes is developed and employed to illustrate the applicability of the proposed framework. In the second related project, we develop a general framework to integrate a novel quantitative measure of resilience and a set of resilience enhancement strategies with process design and operations. The framework identifies a set of disruptive events for a given system, formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model, and solves the problem with a tailored solution algorithm. The applicability of the proposed framework is illustrated through applications on a chemical process network and a shale gas processing system.