Many practical decision processes are dependent on the optimization problem-solving. Over the last few decades, evolutionary computation has become a popular approach for solving complex optimization problems. Because of their tremendous success, many researchers and practitioners think it has introduced a new field that can be recognised as Evolutionary Optimization (EvOpt). As the optimization (conventional!) is a very old and establish discipline, many concepts have been borrowed from this discipline in the development of evolutionary algorithms and, interestingly, both the conventional and evolutionary optimization algorithms are used to solve many similar problems. In this talk, we will mainly discuss (i) what optimization really contributes to the real world decision processes, (ii) what type optimization problems are well suited for evolutionary algorithms and (iii) how the evolutionary algorithm can be configured efficiently. For algorithm configuration, we will provide examples of solution representation, initialisation, search operators, algorithmic parameters, and algorithm framework. Our experiences with a number of practical issues both in real-world decision problems and their solution approaches will be shared. Note that we will limit our discussions on single-objective optimization but under deterministic, stochastic and time-varying situations.