Escalating energy prices and growing consumer concern for sustainable products incentivizes the reduction of energy consumption within the manufacturing sectors. Particularly, the food production industry requires large amounts of heat energy to cook food, of which 9 to 12% is typically wasted. Recovering and reusing waste heat within facilities is a proven method; however, optimizing the waste heat recovery systems (WHRS) can be difficult. Particularly, optimizing WHRS are difficult when process flows vary with respect to time and/or there are multiple objectives desired from the WHRS. Complex systems may be optimized by using a multi-objective evolutionary algorithm (MOEA). The multi-objective design space tradeoffs may then be analyzed using a clustering algorithm to illustrate the cost-benefit of optimizing to one objective versus another. In this work, a case study, using modeled data from an existing cannery, is presented to demonstrate the optimization of the WHRS for the facility. The cannery operates seasonally. During operation raw vegetables and meat are cleaned, cooked, seasoned and canned. A particularly energy- intensive piece of equipment is the retort. The retort steam heats cans to 160°F, for pasteurization, and then water cools them to 85°F. A WHRS will be optimized for recovering waste heat from the retort pasteurization zone to heat process hot water. The MOEA will evaluate for economic, energy performance objectives and size restrictions. These goals include: minimizing up-front costs, maximizing the amount of heat recovered and minimizing the floor space required for the system. While optimizing the system, the MOEA adjusts for seasonal variability, batch processing variability, and a wide range of potential food products. The case study demonstrates that MOEA are useful for illustrating the impact of design objectives and designing WHRS systems.