Operation sequencing as a part of the process planning problem has shown to be a complex optimization challenge in the literature belonging to the class of non-deterministic polynomial problems. Here, operation sequencing problem is represented on a simplified example from the literature and optimized using a metaheuristic approach. Precedence relationships among operations for appropriate features are defined and adjacency matrix is formed. The optimization methodology is based on the modern particle swarm optimization algorithm (mPSO) whose performances are enhanced by chaotic maps and genetic components, such as crossover and two mutation operators. The main focus of this work is on reducing the optimal cost of operation sequence with determination of an appropriate tool and TAD candidate for each operation in a sequence. One case study was conducted in order to test the performances of the proposed algorithm which proved to be very efficient for the simplified operation sequencing problem with excluded machines alternatives.