Numerous multi-objective decision-making problems related with industrial process control engineering such as control and operation performance evaluation are being resolved through human-computer interactions. In regard to the problems that traditional interactive evolutionary computing approaches suffer limited searchin g ability and human’s strong subjectivity in multi-objective-attribute decision-making, a novel affective computing and learning solution adapted to human-computer interaction mechanism is explicitly proposed. Therein, a kind of stimulating response based affective computing models (STAM) is constructed, along with quantitative relations between affective space and humans subjective preferences. Thereafter, affective learning strategies based on genetic algorithms are introduced which are responsible for gradually grasping essentials in human’s subjective judgments in decision-making, reducing human’s subjective fatigue as well as making the decisions more objective and scientific. To exemplify applications of the proposed methods, ad-hoc test functions and PID parameter tuning are suggested to case studies, giving rise to satisfied results and showing validity of the contributions.