The problem of minimizing the makespan of a system with non-identical jobs of different sizes and dynamic arrival times is presented. Since the problem was shown to be NP-hard, heuristics and methods for batching jobs into batches, sorting jobs, sorting batches on machines, assigning jobs to machines, and assigning groups to machines were introduced to solve the problem. 6 heuristics and 4 evolutionary algorithms are defined, each using a special type of chromosome encoding. Deterministic heuristics are divided into 2 categories. The first category groups jobs into batches and then distributes the batches to the machines, while the second category distributes jobs to the machines and then batches the jobs separately for each machine. Evolutionary algorithms are divided according to the way they encode chromosomes. Three algorithms use encoding with which the usual crossover and mutation operators are used, while the last type of encoding has specifically defined mutation operators that obtain the effects of a wide search of the solution space, but also the effect of improving existing solutions. The RPD measure was also introduced, which defines the success of an individual algorithm with a comparison to the optimal solution. A package of methods in the Python programming language was implemented, and heuristics and evolutionary algorithms were tested on examples of problems with different parameters and initial configurations. The results are presented in a way that compares deterministic approaches with evolutionary algorithms.