This paper presents a modified particle swarm optimization algorithm for handling a variety of single and multi objective mixed integer nonlinear optimization problems that have equality and inequality constraints. An efficient multi-objective multi leader particle swarm optimization algorithm is used to handle the extra objective imposed by a novel constraint handling method. In addition, a modified method of handling binary variables is used and the algorithm is adapted to update discrete variables independent from continuous variables using these methods. The algorithm was applied on several well known test problems in the field of chemical engineering including the William Otto process. The results proved the applicability and the efficiency of this method for handling single and multi objective optimization problems in mixed integer and nonlinear decision spaces arising in the field of chemical engineering. (C) 2013 Elsevier Ltd. All rights reserved.