This paper addresses a multi-objective sugarcane harvesting problem in Thailand, where several conflicting objectives and local restrictions are regarded as major obstacles to a sustainable sugar production environment. A multi-objective modeling approach that balances three different objectives of different key supply chain actors, namely (i) maximizing output in terms of total sugar production volume, (ii) maximizing grower equity in terms of a fair harvesting time-slot distribution, and (iii) maximizing supply chain efficiency in terms of a lower variability in resource requirements across the harvesting season, is introduced and solved by a state-of-the-art multi-objective evolutionary genetic algorithm. To better help the algorithm generate efficient solutions forming the Pareto front, two local searches are also embedded and intermittently performed during algorithm execution. Based on the information of an operating mill in Kanchanaburi Province, Thailand, we have found that our approach produces solutions that are close to optimal in terms of production output. Nonetheless, by sacrificing a small amount of production output, these solutions provide significant improvements in terms of grower equity and supply chain resource efficiency, which are crucial for the survivability of involved actors.